Cognitive Impairment in Stable Schizophrenia: Insights from a Large-Scale Chinese Cohort

Cognitive Impairment in Stable Schizophrenia: Insights from a Large-Scale Chinese Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Cognitive Impairment in Stable Schizophrenia: Insights from a Large-Scale Chinese Cohort Chuan Shi, Yuanyuan Zhou, Yumei Cai, Bing Guo, Qiyao Yang, Jun Cai, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8366736/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Objective This study aims to investigate the prevalence of cognitive impairment in patients with stable schizophrenia, analyze factors influencing its occurrence, and examine its impact on functional outcomes. Methods This study includes a sample of 8309 participants diagnosed with stable schizophrenia in China between March and November 2022. Social-demographic and health-related data of the participants were collected. Their cognitive function was assessed by the Chinese Brief Cognitive Test (C-BCT). To evaluate factors associated with cognitive function, Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) were applied. Logistic regression was further employed. Results The proportion of patients with cognitive impairment (defined as cognitive function 1 standard deviation below the population mean on the C-BCT) was 73.68% (N = 6122) in China. Age, disease course, and diabetes were significantly negatively associated with cognitive function, whereas education and waist circumference showed significant positive associations, with significant spatial heterogeneity across the seven geographical regions. Patients treated with 5-HT 1A receptor partial agonists (OR = 0.89, 95% CI: 0.81-0.97) and D2 receptor partial agonists (OR = 0.81, 95% CI: 0.73-0.90) were negatively associated with the occurrence of cognitive impairment. Conclusions This study represents the first large-scale cross-sectional analysis of cognitive function in patients with stable schizophrenia and the initial generalized application of C-BCT cognitive tools. The study found for the first time that the subgroups using 5-HT1A receptor partial agonists or D2 receptor partial agonists showing less impairment. In the future, attention, awareness and interventions should be researched in depth. Biological sciences/Psychology Health sciences/Neuroscience Stable Schizophrenia Cognitive Impairment Chinese Brief Cognitive Test Figures Figure 1 Figure 2 Figure 3 Introduction Schizophrenia, composed of positive, negative, and disorganization syndromes, is a severely disabling mental illness that causes severe social dysfunction and reduces the quality of life in many patients.[1] According to the Global Burden of Disease Study 2019, schizophrenia ranks as the 20 th cause of years lived with disability (YLDs) and holds the third-highest disease burden among mental disorders.[2] Cognitive impairment may be a key underlying factor in schizophrenia.[3]Cognitive impairment is present in 73%-85% of schizophrenia patients, persisting from the prodromal phase to the long-term course of schizophrenia.[4,5] Cross-sectional and longitudinal studies consistently demonstrate significant neurocognitive impairment in both first-episode and stable schizophrenia patients. [6,7]In detail, patients with schizophrenia exhibit impairments across seven domains of the MATRICS Consensus Cognitive Battery (MCCB), among them, the most significant impact are the processing speed and working memory.[8] Furthermore, the immediate family members of patients with schizophrenia have also observed similar cognitive impairment phenomena.. [9]Research has found that not all individuals with schizophrenia experience cognitive impairments,[1,5] but studies on prevalence rates, especially in stable schizophrenia, remain scarce. Cognitive function in schizophrenia is influenced by various factors: 1) Sociodemographic factors. Gender and age are associated with cognitive impairment, with some studies reporting greater severity in males,[6,10] while others find no significant difference.[11] 2) Disease and treatment-related factors. The severity of symptoms, especially negative ones, correlates with the degree of cognitive impairment and antipsychotic treatment[7,12]. While second-generation antipsychotics (SGA) are generally considered to offer more cognitive advantages compared to first-generation antipsychotics (FGA),[13,14] conflicting evidence exists. For instance, one study found that the FGA drug perphenazine improved cognition better than atypical antipsychotics.[15] 3) Metabolic factors. Metabolic syndrome negatively affects cognition, with diabetes consistently linked to cognitive decline.[16,17] However, there is no significant effect in some studies, and the influence of other metabolic factors, [18] such as hypertension, hyperlipidemia, body weight, and waist circumference, remains inconclusive, warranting further investigation.[13,17,19] Currently, data on the prevalence of cognitive impairment in stable schizophrenia are inconsistent, and conclusions regarding its influencing factors vary, likely due to the limited sample sizes in existing studies. To address these gaps, this cross-sectional study utilized a large dataset from China to ascertain the prevalence rate of cognitive impairment in stable schizophrenia, identify factors associated with cognitive dysfunction with particular attention to spatial heterogeneity, and examine the impacts of cognitive function on social outcomes. This study represents the first large-scale application of the C-BCT, a locally adapted and simplified digital cognitive assessment tool. We mapped, for the first time, a distribution of cognitive impairments among stable-phase schizophrenia patients in China and conducted a comprehensive analysis of factors associated with cognitive impairments. Methods Participants and Procedure This study conducted a multicenter cross-sectional survey at 107 psychiatric hospitals across China from March to November 2022. To ensure the representativeness of the samples, hospitals were recruited from all seven geographical regions, including 26 in North China, 17 in Northeast China, 4 in Northwest China, 29 in East China, 15 in Central China, 12 in Southwest China, and 4 in South China. Recruitment sites within each hospital included inpatient wards or outpatient departments, and eligible patients were enrolled until either 100 participants were recruited per hospital or the total recruitment target was reached. There is a diagnosis of schizophrenia for our inclusion criteria according to the International Statistical Classification of Diseases and Related Health Problems, 10 th Revision (ICD-10), be in a stable phase of the condition (defined by the severity of illness of Clinical Global Impression (CGI-S) score of ≤ 3 and no medication adjustments for at least one month), and be aged 18 to 60 years. Exclusion criteria included comorbid neurodevelopmental retardation, organic brain disease, severe physical illness that precludes cooperation with investigators, alcohol or drug abuse lasting more than 10 years, psychoactive substance dependence, extreme states (excitement, stupor or suicide), history of head trauma with loss of consciousness for more than an hour, pregnancy or breastfeeding, and electroconvulsive therapy within the 6 months before enrollment. Furthermore, social demographic data were collected using a self-designed electronic case report form, and a cognitive assessment was performed. A total of 9,940 participants participated in the study. The research protocol was approved by the ethical review board of Peking University Sixth Hospital (2021 Ethics Review No. 72). Indicator Measurements Outcome Definitions This study selected C-BCT, a simplified cognitive assessment tool developed by the Delphi method, which is highly correlated with MCCB (the gold standard for schizophrenia cognitive assessment, r = 0.76). C-BCT showed good internal consistency with a Cronbach’s α of 0.75, The Intraclass Correlation Coefficient (ICC) values for the four subtests and composite scaled score ranged from 0.62 to 0.76. It enables electronic testing within 15 minutes without requiring trained evaluators, offering an efficient, convenient, and scalable solution for large-scale studies.( 20 , 21 ) C-BCT comprises four sub-tests: 1) Trail Making Test: respondents connect consecutive numbers placed randomly on an electronic screen. System auto-detects errors, alerts, and allows continuation. Scored by completion time. 2) Symbol Coding: respondents match numbers to symbols as quickly as possible within 90 seconds. Scored by total correct matches. 3) Continuous Performance Test: three sets of varying animal combinations flash continuously. Respondents click at two consecutive identical stimuli. Scored by the number of correct and incorrect responses. 4) Digit Span: Voice-broadcast number sequences (2–9 digits, increasing length) play at set pace. Forward span: type in heard order; backward span: type in reverse. Scored according to the length of the remembered number. It is operated on tablets or smartphones and assesses the neurocognitive domains, including processing speed, sustained attention, verbal working memory, reasoning, and problem-solving. Cognitive function is graded as normal, mild, moderate, or severe based on standardized norms which were derived from 723 healthy participants in China.[20,21] C-BCT scores are obtained by converting the raw scores of each subtest to a mean of 10 with a standard deviation of 3 and then summing them for each participant. Normative Data was used to establish a cognitive score model to predict C-BCT scores, and then C-BCT scores were converted into standardized scores with a mean of 50 and standard deviation of 10 as T scores. Cognitive impairment is classified by the T score: A T score of 40 or higher is normal, 35-39 is mild, 25-29 is moderate, and 24 or lower is severe. Related Outcome Self-reported employment status was used as the functional outcome, divided into employed, unemployed, and retired. However, since retirement could be due to either age or illness, only employment and unemployment were considered functional outcomes. Consequently, 441 retired samples were excluded from the analysis of function outcomes. Influence Factors Information on the date of onset, the date of first treatment, gender, age, education, and the presence of comorbidities, including diabetes mellitus and hypertension, were obtained through self-reports or medical records of the patient. The course of disease referred to the duration from onset to enrollment, and the duration of untreated was defined as the time from onset to first treatment. The level of education was divided into illiteracy, primary school, junior high school, high school (or technical secondary school), university (or junior college), and postgraduate or above. Regional factors were determined based on the geographical location of the enrolled hospitals, which were aligned with the seven geographical regions in China. Waist circumference, height, and weight were obtained by physical examinations, and body mass index (BMI) was calculated as weight divided by the square of height. According to the criteria for metabolic syndrome and obesity in China, a waist circumference of ≥ 90 cm in men or ≥ 85cm in women, and a BMI of ≥ 28 were classified as abnormal.[22,23] Antipsychotic drugs were classified according to their pharmacological receptor action, whether they had partial dopamine 2 receptor (D2) excitability, partial serotonin 1A receptor (5-HT 1A ) excitability, and antagonism of serotonin 2A receptor (5-HT 2A ). Statistical Analysis Descriptive statistics were initially conducted for cognitive assessment results and potentially related factors, including social-demographic attributes like gender, age, and education level, as well as health-related attributes like the course of the disease, time elapse to treatment, and comorbid hypertension. Subsequently, Chi-square tests were applied to examine the prevalence in different subgroups. To explore the factors in relation with cognitive function across the seven geographical regions, we employed OLS regression and GWR, conducted by ArcGIS 10.8 software. Firstly, the Moran Index was computed to evaluate the global spatial autocorrelation of the C-BCT scores. Next, all variables were standardized by Z-score to ensure comparability across different measures. OLS regression was then conducted to identify the key factors associated with cognitive performance, with the Variance Inflation Factor (VIF) test confirming no multicollinearity among the variables. Finally, GWR was applied, using the adaptive method as the space kernel function type and the Akaike information criterion (AIC) as the bandwidth method. Antipsychotics were divided into different types according to the pharmacological receptors affected, and taken as a binary classification variable for the ordinal logistics analysis, with the degree of cognitive impairment as the dependent variable. This analysis controlled for demographic factors, including age, gender, and educational level. Logistic regression analysis was performed for cognitive function and functional outcome indicators, controlling the confounding factors, including age, sex, and education level. This analysis, along with the descriptive statistics and the Chi-square test, was performed by SPSS 24. Role of the funding source The funders of the study played no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit it for publication. Results Descriptive Statistics Eligibility criteria were applied to identify participants by reviewing their compliance with the inclusion criteria, followed by screening for the availability of complete cognitive assessment data. Ultimately, 8309 qualified individuals were included for subsequent analysis (Figure 1). Fig. 1 Screening process and geographical distribution of participants The spatial distribution of participants was visualized based on the standard map (Review No. GS(2024)0650) obtained from the National Geographic Information Public Service Platform of China. Among the 8309 valid participants, 5132 were males (61.76%) and 3177 were females (38.24%), with an average age of 41.23 years (SD = 11.25). The average course of disease and years of education were 12.22 years (SD = 9.95) and 9.93 years (SD = 3.82), respectively. Regarding the duration of the disease before treatment, the average was 1.35 years (SD = 3.52) among all participants. The participants were recruited from seven geographical regions across China, with the cohort size as well as cognitive and non-cognitive impairment ratios for each region summarized in Table 1. Our data reveal significant statistical differences in the proportion of cognitive impairment among various factors, including age, course of disease, duration of untreated, education, region, BMI, and waist circumference. Specifically, higher education level was correlated with a lower prevalence of cognitive impairment ( p < 0.001), and patients with abnormal waist circumference ( p < 0.001) and BMI ( p < 0.05) had a lower prevalence of cognitive impairment compared to those within the normal range (Table 1). Table 1 . Descriptive statistics of the cohort N Cognitive impairment Non-cognitive impairment P Total Gender, N (%) 0.056 Female 3177 (38.24) 2378 (74.85) 799 (25.15) Male 5132 (61.76) 3744 (72.95) 1388 (27.05) Age (y), Mean ± SD 41.23 ± 11.25 41.96 ± 11.19 39.21 ± 11.18 ＜0.001 Course of disease (y), Mean ± SD 12.22 ± 9.95 12.54 ± 10.16 11.35 ± 9.28 ＜0.001 Duration of untreated (y), Mean ± SD 1.35 ± 3.52 1.40 ± 3.64 1.22 ± 3.19 0.037 Education (y), Mean ± SD 9.93 ± 3.82 9.61 ± 3.78 10.82 ± 3.76 ＜0.001 Illiteracy, N (%) 198 (2.38) 164 (82.83) 34 (17.17) Primary school, N (%) 1292 (15.55) 1067 (82.59) 225 (17.41) Junior high school, N (%) 3072 (36.97) 2339 (76.14) 733 (23.86) High school (or technical secondary school), N (%) 2126 (25.59) 1536 (72.25) 590 (27.75) University (or junior college), N (%) 1555 (18.71) 977 (62.83) 578 (37.17) Postgraduate or above, N (%) 11 (0.13) 6 (54.55) 5 (45.45) Region, N (%) ＜0.001 Northeast China 1655 (19.92) 1418 (85.68) 237 (14.32) North China 1579 (19.00) 1033 (65.42) 546 (34.58) Northwest China 375 (4.51) 255 (68.00) 120 (32.00) East China 2187 (26.32) 1576 (72.06) 611 (27.94) Central China 1182 (14.23) 788 (66.67) 394 (33.33) Southwest China 931 (11.20) 748 (80.34) 183 (19.66) South China 400 (4.81) 304 (76.00) 96 (24.00) BMI (kg/m 2 ), Mean ± SD 23.81 ± 5.73 0.011 Normal, N (%) 7085 (85.27) 5258 (74.21) 1827 (25.79) Abnormal, N (%) 1148 (13.83) 811 (70.64) 337 (29.36) Waist circumference (cm), Mean ± SD 82.37 ± 21.57 ＜0.001 Normal, N (%) 4856 (58.44) 3709 (76.38) 1147 (23.62) Abnormal, N (%) 2829 (34.05) 1964 (69.42) 865 (30.58) Hypertension, N (%) 0.623 Yes 459 (5.52) 343 (74.73) 116 (25.27) No 7795 (93.81) 5744 (73.69) 2051 (26.31) Diabetes, N (%) 0.713 Yes 443 (5.33) 330 (74.49) 113 (25.51) No 7810 (93.99) 5756 (73.70) 2054 (26.30) Prevalence of Cognitive Impairment As the research indicates, according to C-BCT scores, 73.68% of stable schizophrenia patients in China exhibited varying degrees of cognitive impairment. 22.07%, 41.95%, and 9.65% of the patients had mild, moderate, and severe defects, respectively. The prevalence of cognitive impairment in each sub-test was as follows: 33.25% in the Trail Making Test, 54.3% in Symbol Coding, 52.87% in the Continuous Performance Test, and 64.11% in the Digit Span. Factors Associated with Cognitive Function There were significant differences in cognitive function of patients with stable schizophrenia in different geographic regions. The C-BCT scores of stable schizophrenia patients in central China were the highest, while those in southwest China were the lowest (Figure 2). The Moran’s Index of C-BCT scores was 0.019, with a z-score of 91.085 and a p-value of 0.000, indicating statistically significant positive spatial autocorrelation of cognitive function. Fig. 2 Spatial distribution of average C-BCT scores of stable schizophrenia patients across the seven geographical regions To explore the factors influencing cognitive function across the seven geographical regions, we employed OLS regression. Table 2 summarizes the OLS regression results for factors associated with cognitive function. The Adjusted R 2 value reveals that the model accounts for over 22% of the variance in cognitive scores, suggesting a moderate explanatory power (Table 2). Cognitive function was positively correlated with years of education (coefficient = 0.295, p < 0.001), while negatively correlated with the course of disease (coefficient = -0.114, p < 0.001) and age (coefficient = -0.225, p < 0.001) (Table 2). Patients with abnormal waist circumference had better cognitive performance (coefficient = 0.069, p < 0.001), whereas those diagnosed with diabetes showed lower cognitive scores (coefficient = -0.028, p < 0.01). Meanwhile, the VIF values for all variables were below 5.0, indicating that multicollinearity is not a concern in the analysis. Moreover, other factors, including gender, BMI comorbid hypertension, and duration of untreated, were eliminated from the model due to their non-significant influence on cognitive scores. In addition, the Koenker (BP) Statistic and Joint Wals Statistic were both statistically significant ( p < 0.001), confirming that the overall model is significant and that the five factors exhibit spatial heterogeneity in their effects on cognitive function (Table 2). Table 2 . Results of the OLS regression model Variable Coefficient 95% CI P Lower Upper Intercept 0.000 -0.020 0.020 0.999 Age -0.225 -0.249 -0.201 ＜0.001 Education 0.295 0.275 0.316 ＜0.001 Course of disease -0.114 -0.137 -0.091 ＜0.001 Waist circumference 0.069 0.049 0.089 ＜0.001 Diabetes -0.028 -0.049 -0.008 0.009 Diagnostic information R 2 0.222 Adjusted R 2 0.221 AIC 19518.903 Note: CI: Confidence Interval. We further assessed the factors influencing cognitive function across different regions using GWR. The model explains approaching 40% of changes in cognitive scores, an improvement of nearly 18% in explanatory power compared to the OLS regression model (Table 3). Meanwhile, the AIC value decreased by 1656.591, indicating that the GWR model provides a substantially better fit and more effectively captures spatial variation in cognitive performance. Age exhibited a significantly negative association with cognitive function in Southwest China and a mildly negative relationship in Northeast China (Figure 3A). Education showed a stronger positive association with cognitive performance in South and Northwest China, though the association appeared weaker in Northeast China (Figure 3B). The course of disease was predominantly associated with cognition in the Northwest and Northeast regions (Figure 3C). Although waist circumference was significantly associated with cognitive performance, its influence was relatively weak, with minimal spatial differentiation in the coefficients (Figure 3D). Diabetes showed a substantial negative correlation in South China, while it exhibited a weak correlation in Northwest and Northeast China (Figure 3E). Table 3 . Results of the GWR model Variable Coefficient Standard Deviation Mean Minimum Maximum Age -0.179 -0.623 0.133 0.136 Education 0.303 -0.023 0.621 0.151 Course of disease -0.110 -0.789 0.255 0.165 Waist circumference 0.126 -0.199 0.473 0.101 Diabetes -0.036 -0.227 0.133 0.076 Diagnostic information R 2 0.423 Adjusted R 2 0.396 AIC 17862.312 Fig. 3 Impacts of five factors on cognitive function across the seven geographical regions Effects of Drugs on Cognitive Impairment Medication usage was analyzed collectively for each subject. The most commonly by drugs were olanzapine, risperidone, and clozapine, accounting for 28.48% (N = 2366), 21.95% (N = 1824), and 21.88% (N = 1818), respectively (Table 4). Patients using 5-HT 1A receptor partial agonists (OR = 0.89, 95% CI: 0.81-0.97) and D2 receptor partial agonists (OR = 0.81, 95% CI: 0.73 -0.90) were negatively associated with the occurrence of cognitive impairment compared to those without them (Table 5). The FGA or SGA did not significantly associate with the degree of cognitive impairment compared with the combination of FGA and SGA. Similarly, the use of 5-HT 2A receptor antagonists showed no significant association with cognitive impairment ( p ＞ 0.05). Table 4 . Antipsychotic drug use in all participants Drug N Percentage Drug N Percentage Olanzapine 2366 28.48 Paliperidone 340 4.09 Clozapine 1824 21.95 Perospirone 257 3.09 Risperidone 1818 21.88 Perphenazine 102 1.23 Aripiprazole 1306 15.72 Lurasidone 91 1.10 Quetiapine 810 9.75 Blonanserin 74 0.89 Ziprasidone 703 8.46 Haloperidol 59 0.71 Amisulpride 613 7.38 Table 5 . Association between types of antipsychotics and degrees of cognitive impairment OR 95% CI P Lower Upper FGA 0.92 0.46 1.84 0.804 SGA 0.86 0.62 1.19 0.361 D2 receptor partial agonist 0.81 0.73 0.90 ＜0.001 5-HT 1A receptor partial agonist 0.89 0.81 0.97 0.006 5-HT 2A receptor antagonist 1.20 0.96 1.50 0.116 Note: FGA: first generation antipsychotics; SGA: second generation antipsychotics; D 2 receptor partial agonist includes Aripiprazole; 5-HT 1A receptor partial agonists include Aripiprazole, Quetiapine, Lurasidone, Perospirone, Ziprasidone; 5-HT 2A receptor antagonists include Olanzapine, Risperidone, Clozapine, Aripiprazole, Quetiapine, Ziprasidone, Paliperidone, Perospirone, Lurasidone, Blonanserin. Impact of Cognitive Impairment on Functional Outcomes in patients with stable schizophrenia The logistic regression results reveal that cognitive impairment was significantly associated with unemployment (Table 6). After controlling for gender, age, and educational level, mild (OR = 1.95, 95% CI: 1.46-2.59), moderate (OR = 1.47, 95% CI: 1.10-1.98) and severe cognitive impairment(OR = 1.46, 95% CI: 1.10-1.93) were all significantly associated with unemployment compared to normal cognition(Table 6). Table 6 . The impact of cognitive impairment on employment outcomes Employed Unemployed Retired OR (95% CI) P Normal 361 1711 105 Ref Mild 227 1506 96 1.95 (1.46-2.59) ＜0.001 Moderate 382 2908 188 1.47 (1.10-1.98) 0.01 Severe 63 699 38 1.46 (1.10-1.93) 0.009 Discussion As the biggest single large-scale cross-sectional study of cognitive function in patients with stable schizophrenia, it enhances the representativeness of the results and provides valuable insight into the cognitive status of the Chinese population. Notably, this study marks the first large-scale generalized application of the C-BCT cognitive tool. Due to the simplicity and standardized electronic operation, C-BCT is well-suited for brief cognitive screening and has proven effective in research involving patients with schizophrenia, which provides an affordable, reliable tool for large-scale screening of schizophrenia, especially in developing countries. This highlights its potential for broader application in future studies across various disease prevalence. Building on previous studies that show over half of patients with schizophrenia experience cognitive impairment,[24] this study further quantified the high incidence of such impairment in stable schizophrenia, indicating that cognitive impairment persists even after symptoms have subsided. Among the four C-BCT sub-tests, the proportion of cognitive impairment was over 50% for both the Symbol Coding and Continuous Performance Test, with the Digit Span test showing the highest rate of impairment at 64.11%. These tests measure information processing speed and executive function, sustained and focused attention, and auditory and verbal working memory, respectively. This aligns with previous findings that individuals with schizophrenia have more severe impairments in processing speed, verbal memory, and working memory.[24] Additionally, other studies and reviews consistently emphasize that cognitive impairment in schizophrenia is particularly pronounced in processing speed and working memory.[8,25] The analysis of cognition-related factors revealed that age, disease course, and diabetes were significantly negatively correlated with cognitive function, while higher education level and waist circumference were positively correlated with cognitive function. Similar to previous findings, age and disease course are negatively correlated with cognitive function, while higher education level is positively correlated. Prior studies also showed that patients with cognitive impairment tend to have longer disease durations, higher disease severity scores, and higher medication doses.[26]Furthermore, subgroups with impaired cognitive function had fewer years of education than those with normal cognitive function.[27] For metabolism-related factors, patients with diabetes had worse cognitive performance, while those with abnormal waist circumference had better cognitive performance. A meta-analysis supports that schizophrenia patients with diabetes experience more severe cognitive impairment.)[19] Some studies suggest that obesity is not associated with cognition,[17 while others indicate that schizophrenia with obesity has poorer cognitive performance.[19] Another meta-analysis suggests that high waist circumference itself could be a risk factor for cognitive impairment and dementia.[28]Conversely, this study showed that individuals with abnormal waist circumference demonstrated better cognitive performance. This may be influenced by psychiatric medications, which often affect metabolism and waist circumference, as well as the confounding effects of efficacy and disease severity. A study has shown that the response to clozapine is correlated with waist circumference, with clozapine-resistant groups exhibiting smaller waist circumference.[29] It is hypothesized that the increase in waist circumference may be more pronounced in patients with a better drug response. These patients tend to have a better treatment response and less cognitive impairment, which may explain why the group with abnormal waist circumference has less cognitive impairment. Interestingly, we observed significant spatial heterogeneity in cognitive function across the seven geographical regions, an observation rarely reported in previous studies. This finding adds a new dimension and enhances the resolution of our analysis. The possible contributing factors may be: 1) Age differences. Participants from Central China had the youngest average age, which may account for their highest cognitive performance, as cognitive function typically declines with age. 2) Economic disparity. Research indicates that economic recession and poverty may be associated with cognitive decline. [30,31]The relatively underdeveloped economy in Southwest China likely contributed to the lowest cognitive performance observed in this region. 3) Prevalence of Mandarin. Mandarin proficiency varies across China, with the Western region showing lower prevalence than other regions. [32]Since the C-BCT cognitive assessment tools used in our study were administered in Mandarin, lower Mandarin proficiency in the Western region may have led to difficulties in fully understanding the C-BCT cognitive assessment tools, resulting in lower cognitive scores. The analysis of antipsychotic factors, classified by receptor action, revealed that subgroups using 5-HT 1A receptor partial agonists or D2 receptor partial agonists had less cognitive impairment. This may be related to the role of the 5-HT 1A receptor in learning and memory. [33,34]Aripiprazole and quetiapine (with a partial 5-HT 1A agonist) can also improve cognitive function.[13] This study also innovatively investigated whether combination therapy is associated with cognitive function and observed no significant difference in cognitive function between patients treated with the combination drug and those treated with monotherapy, whether FAG or SAG, suggesting that combination therapy can be used in clinical practice when necessary, without undue concern about exacerbating cognitive impairment. However, these findings warrant further investigations. As a real-world cross-sectional study, the reliability of the results may be affected by the fact that most patients (97%) used combination therapy. Among stable schizophrenia patients, cognitive impairment has been shown to be associated with unemployment, irrespective of the severity of the impairment. This is consistent with existing research concluding cognitive function as a predictor of employment outcomes. Interestingly, patients with mild cognitive impairment showed a higher odds ratio (OR) for unemployment compared to those with moderate or severe impairment, which can be attributed to a combination of higher expectations, lack of support, the transitional nature of Mild Cognitive Impairment, and insufficient workplace accommodations. Given that cognitive remediation training can enhance functional outcomes, [35]including employment,[36] it is crucial to prioritize such interventions to improve the prognosis of schizophrenia patients, particularly those with mild cognitive impairment. Due to the impacts of education levels and disease courses on cognitive function, it is essential to prioritize educational programs and early interventions to mitigate cognitive impairment by providing educational support and shortening illness duration. The study also validated the relationship between cognitive function and functional outcomes, emphasizing the importance of addressing cognitive impairment in clinical practice and giving interventions to improve patients’ long-term quality of life. Meanwhile, this study also has some limitations. As a cross-sectional study, it cannot establish causality. For example, while the results indicate that education may act as a protective factor for cognitive function, it is also possible that cognitive impairment in patients with schizophrenia prevents them from completing their education, leading to lower educational attainment. Thus, longitudinal studies are needed to explore these relationships more thoroughly. In addition, our study of employment outcomes is brief, and future research should include more specific measures such as hours worked and income earned, etc. Patients' disease-related information—such as the date of onset, the date of first treatment, and the presence of comorbidities—was obtained through self-reporting, a method inherently prone to recall bias that may introduce inaccuracies. Although differences in cognitive function among patients with schizophrenia have been observed across different regions, the underlying causes remain unclear. Future research could explore factors such as economic status, education, healthcare, diet, and culture to gain a deeper understanding. Conclusion This study represents the first large-scale cross-sectional analysis of cognitive function in patients with stable schizophrenia and the initial generalized application of C-BCT cognitive tools. Among 8309 patients in China, 73.68% (N = 6122) exhibited cognitive impairment. Cognitive function was significantly negatively correlated with age, disease course, and diabetes, while positively correlated with higher education level and waist circumference. These correlations showed significant spatial heterogeneity across seven geographical regions. Additionally, the types of antipsychotic drugs were related to cognitive impairment, with subgroups using 5-HT 1A receptor partial agonists or D2 receptor partial agonists showing less impairment. Mild, moderate, and severe cognitive impairments are all significantly associated with unemployment outcomes. These findings emphasize the critical role of cognitive function in patients with schizophrenia, highlighting the need for increased attention, awareness, and interventions. Declarations INTERPRETATION Early awareness and interventions are critical for combating the high prevalence of cognitive impairment to improve the quality of life and employment in stable schizophrenia. The spatial heterogeneity also points out the need for addressing economic disparities and more healthcare support. CONTRIBUTORS Chuan Shi, Xin Yu, and Gong Chen conceived the study. Yuanyuan Zhou, Chuan Shi, and Xin Yu collected data. Yuanyuan Zhou, Bin Guo, Qiyao Yang, and Zhiquan Li performed formal data analysis. Yuanyuan Zhou and Yumei Cai formulated the initial version of the manuscript. Chi Zhang, Zhiquan Li, Qiyao Yang, Jun Cai, and Vilhelm A. Bohr reviewed and revised the manuscript. All authors reviewed and approved the final version of the manuscript. AVAILABILITY OF DATA AND MATERIALS The majority of the information collected or analyzed during this study is presented and discussed in this article. The Peking University Institute of Mental Health’s Ethics Committee reviewed and approved all studies involving participants. The patients/participants gave their written informed consent to take part in this study. DECLARATION OF INTERESTS We declare no competing interests. References Jauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet. 2022;399(10323):473-486. GBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137-150. Halverson TF, Orleans-Pobee M, Merritt C, Sheeran P, Fett AK, Penn DL. Pathways to functional outcomes in schizophrenia spectrum disorders: Meta-analysis of social cognitive and neurocognitive predictors. Neurosci Biobehav Rev. 2019;105:212-19. Palmer BW, Heaton RK, Paulsen JS, et al. Is it possible to be schizophrenic yet neuropsychologically normal? Neuropsychology. 1997;11(3):437-446. Reichenberg A, Harvey PD, Bowie CR, et al. Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders. Schizophr Bull. 2009;35(5):1022-29. Chen S, Liu Y, Liu D, Zhang G, Wu X. The difference of social cognitive and neurocognitive performance between patients with schizophrenia at different stages and influencing factors. Schizophr Res Cogn. 2021;24:100195. Gold S, Arndt S, Nopoulos P, O'Leary DS, Andreasen NC. Longitudinal study of cognitive function in first-episode and recent-onset schizophrenia. Am J Psychiatry. 1999;156(9):1342-8. Kern RS, Gold JM, Dickinson D, et al. The MCCB impairment profile for schizophrenia outpatients: results from the MATRICS psychometric and standardization study. Schizophr Res. 2011;126(1-3):124-131. da Motta C, Pato MT, Barreto Carvalho C, Castilho P. The neurocognitive and functional profile of schizophrenia in a genetically homogenous European sample. Psychiatry Res. 2021;304:114140. Zhao N, Wang XH, Kang CY, et al. Sex differences in association between cognitive impairment and clinical correlates in Chinese patients with first-episode drug-naïve schizophrenia. Ann Gen Psychiatry. 2021;20(1):26. Krysta K, Murawiec S, Klasik A, Wiglusz MS, Krupka-Matuszczyk I. Sex-specific differences in cognitive functioning among schizophrenic patients. Psychiatr Danub. 2013;25 Suppl 2:S244-46. Cao X, Chen S, Xu H, Wang Q, Zhang Y, Xie S. Global functioning, cognitive function, psychopathological symptoms in untreated patients with first-episode schizophrenia: A cross-sectional study. Psychiatry Res. 2022;313:114616. MacKenzie NE, Kowalchuk C, Agarwal SM, et al. Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia. Front Psychiatry. 2018;9:622. Woodward ND, Purdon SE, Meltzer HY, Zald DH. A meta-analysis of neuropsychological change to clozapine, olanzapine, quetiapine, and risperidone in schizophrenia. International Journal of Neuropsychopharmacology. 2005;8(3):457-72. Baldez DP, Biazus TB, Rabelo-da-Ponte FD, et al. The effect of antipsychotics on the cognitive performance of individuals with psychotic disorders: Network meta-analyses of randomized controlled trials. Neuroscience & Biobehavioral Reviews. 2021;126:265-275. Akdede BB, Alptekin K, Bora E. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychological Medicine. 2017;47(6):1030-40. Hagi K, Nosaka T, Dickinson D, et al. Association Between Cardiovascular Risk Factors and Cognitive Impairment in People With Schizophrenia: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2021;78(5):510-18. Meyer JM, Nasrallah HA, McEvoy JP, et al. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial: Clinical comparison of subgroups with and without the metabolic syndrome. Schizophrenia Research. 2005;80(1):9-18. Bora E, Akdede BB, Alptekin K. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychol Med. 2017;47(6):1030-40. Ke M, Xin Y, Chunbo L, Chuan S. A Delphi study of the Brief Cognitive Assessment for Schizophrenia. Chinese Mental Health Journal. 2020;34(9):736-40. Ye S, Xie M, Yu X, et al. The Chinese Brief Cognitive Test: Normative Data Stratified by Gender, Age and Education. Front Psychiatry. 2022;13:933642. Pan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373-92. Junren Z, Runlin G, Shuiping Z, Guoping L, Dong Z, Jianjun L. Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China (2016 Revision). Chinese Circulation J. 2016;31(10):937-392. Gebreegziabhere Y, Habatmu K, Mihretu A, Cella M, Alem A. Cognitive impairment in people with schizophrenia: an umbrella review. Eur Arch Psychiatry Clin Neurosci. 2022;272(7):1139-55. McCleery A, Nuechterlein KH. Cognitive impairment in psychotic illness: prevalence, profile of impairment, developmental course, and treatment considerations . Dialogues Clin Neurosci. 2019;21(3):239-48. Ortiz-Gil J, Pomarol-Clotet E, Salvador R, et al. Neural correlates of cognitive impairment in schizophrenia. Br J Psychiatry. 2011;199(3):202-10. Carruthers SP, Van Rheenen TE, Karantonis JA, Rossell SL. Characterising Demographic, Clinical and Functional Features of Cognitive Subgroups in Schizophrenia Spectrum Disorders: A Systematic Review. Neuropsychology Rev. 2022;32(4):807-27. Tang X, Zhao W, Lu M, et al. Relationship between Central Obesity and the incidence of Cognitive Impairment and Dementia from Cohort Studies Involving 5,060,687 Participants. Neurosci Biobehav Rev. 2021;130:301-313. Hönig G, Daray FM, Rodante D, Drucaroff L, Gutiérrez ML, Lenze M, García Bournissen F, Wikinski S. Body mass index, waist circumference, insulin, and leptin plasma levels differentiate between clozapine-responsive and clozapine-resistant schizophrenia. J Psychopharmacol. 2023;37(10):1023-1029. Leist AK, Hessel P, Avendano M. Do economic recessions during early and mid-adulthood influence cognitive function in older age? J Epidemiol Community Health. 2014;68(2):151-8. Mani A, Mullainathan S, Shafir E, Zhao J. Poverty impedes cognitive function. Science. 2013;341(6149):976-80. Weiqi Y. Current Situation and Development of National Putonghua Proficiency. Applied Linguistics. 2018;(2):99-07. Glikmann-Johnston Y, Saling MM, Reutens DC, Stout JC. Hippocampal 5-HT1A Receptor and Spatial Learning and Memory. Front Pharmacol. 2015;6:289. Ogren SO, Eriksson TM, Elvander-Tottie E, et al. The role of 5-HT(1A) receptors in learning and memory. Behav Brain Res. 2008;195(1):54-77. Medalia A, Saperstein AM. Does cognitive remediation for schizophrenia improve functional outcomes? Curr Opin Psychiatry. 2013;26(2):151-57. McGurk SR, Mueser KT, Xie H, et al. Cognitive Enhancement Treatment for People With Mental Illness Who Do Not Respond to Supported Employment: A Randomized Controlled Trial. Am J Psychiatry. 2015;172(9):852-61. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8366736","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":588627141,"identity":"cf8b52f9-c8af-4d2b-9e98-b1ac7936f799","order_by":0,"name":"Chuan 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13:28:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8366736/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8366736/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102598028,"identity":"fdc8a0e9-9f8e-41e7-b77a-2dcc0e2c2ff4","added_by":"auto","created_at":"2026-02-13 12:27:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88824,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eScreening process and geographical distribution of participants\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8366736/v1/65af0ba259f6daccc107c833.jpg"},{"id":102598051,"identity":"8f2e55fe-5fc2-4f45-9767-d497dfca2196","added_by":"auto","created_at":"2026-02-13 12:27:09","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":71515,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of average C-BCT scores of stable schizophrenia patients across the seven geographical regions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8366736/v1/e3d0557620e59b47c8bb34bb.jpg"},{"id":102598100,"identity":"5ee5cdfa-49c9-4102-8756-e930626f330c","added_by":"auto","created_at":"2026-02-13 12:27:15","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":249086,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImpacts of five factors on cognitive function across the seven geographical regions\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Picture3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8366736/v1/9e459e355b36c96dd962095f.jpg"},{"id":102747411,"identity":"6e128fe8-99da-41b6-8ec0-d01a2825c9a4","added_by":"auto","created_at":"2026-02-16 09:04:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1713493,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8366736/v1/73874a8d-ed84-4fdf-884c-097cc2dd1991.pdf"}],"financialInterests":"The authors have declared there is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose","formattedTitle":"Cognitive Impairment in Stable Schizophrenia: Insights from a Large-Scale Chinese Cohort","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSchizophrenia, composed of positive, negative, and disorganization syndromes, is a severely disabling mental illness that causes severe social dysfunction and reduces the quality of life in many patients.[1] According to the Global Burden of Disease Study 2019, schizophrenia ranks as the 20\u003csup\u003eth\u003c/sup\u003e cause of years lived with disability (YLDs) and holds the third-highest disease burden among mental disorders.[2] Cognitive impairment may be a key underlying factor in schizophrenia.[3]Cognitive impairment is present in 73%-85% of schizophrenia patients, persisting from the prodromal phase to the long-term course of schizophrenia.[4,5] Cross-sectional and longitudinal studies consistently demonstrate significant neurocognitive impairment in both first-episode and stable schizophrenia patients. [6,7]In detail, patients with schizophrenia exhibit impairments across seven domains of the MATRICS Consensus Cognitive Battery (MCCB), among them, the most significant impact are the processing speed and working memory.[8] Furthermore, the immediate family members of patients with schizophrenia have also observed similar cognitive impairment phenomena.. [9]Research has found that not all individuals with schizophrenia experience cognitive impairments,[1,5] but studies on prevalence rates, especially in stable schizophrenia, remain scarce.\u003c/p\u003e\n\u003cp\u003eCognitive function in schizophrenia is influenced by various factors: 1) Sociodemographic factors. Gender and age are associated with cognitive impairment, with some studies reporting greater severity in males,[6,10] while others find no significant difference.[11] 2) Disease and treatment-related factors. The severity of symptoms, especially negative ones, correlates with the degree of cognitive impairment and antipsychotic treatment[7,12]. While second-generation antipsychotics (SGA) are generally considered to offer more cognitive advantages compared to first-generation antipsychotics (FGA),[13,14] conflicting evidence exists. For instance, one study found that the FGA drug perphenazine improved cognition better than atypical antipsychotics.[15] 3) Metabolic factors. Metabolic syndrome negatively affects cognition, with diabetes consistently linked to cognitive decline.[16,17] However, there is no significant effect in some studies, and the influence of other metabolic factors, [18] such as hypertension, hyperlipidemia, body weight, and waist circumference, remains inconclusive, warranting further investigation.[13,17,19]\u003c/p\u003e\n\u003cp\u003eCurrently, data on the prevalence of cognitive impairment in stable schizophrenia are inconsistent, and conclusions regarding its influencing factors vary, likely due to the limited sample sizes in existing studies. To address these gaps, this cross-sectional study utilized a large dataset from China to ascertain the prevalence rate of cognitive impairment in stable schizophrenia, identify factors associated with cognitive dysfunction with particular attention to spatial heterogeneity, and examine the impacts of cognitive function on social outcomes. This study represents the first large-scale application of the C-BCT, a locally adapted and simplified digital cognitive assessment tool. We mapped, for the first time, a distribution of cognitive impairments among stable-phase schizophrenia patients in China and conducted a comprehensive analysis of factors associated with cognitive impairments.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e\u003cstrong\u003eParticipants and Procedure\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThis study conducted a multicenter cross-sectional survey at 107 psychiatric hospitals across China from March to November 2022. To ensure the representativeness of the samples, hospitals were recruited from all seven geographical regions, including 26 in North China, 17 in Northeast China, 4 in Northwest China, 29 in East China, 15 in Central China, 12 in Southwest China, and 4 in South China. Recruitment sites within each hospital included inpatient wards or outpatient departments, and eligible patients were enrolled until either 100 participants were recruited per hospital or the total recruitment target was reached. There is a diagnosis of schizophrenia for our inclusion criteria according to the International Statistical Classification of Diseases and Related Health Problems, 10\u003csup\u003eth\u003c/sup\u003e Revision (ICD-10), be in a stable phase of the condition (defined by the severity of illness of Clinical Global Impression (CGI-S) score of\u0026nbsp;≤\u0026nbsp;3 and no medication adjustments for at least one month), and be aged 18 to 60 years. Exclusion criteria included comorbid neurodevelopmental retardation, organic brain disease, severe physical illness that precludes cooperation with investigators, alcohol or drug abuse lasting more than 10 years, psychoactive substance dependence, extreme states (excitement, stupor or suicide), history of head trauma with loss of consciousness for more than an hour, pregnancy or breastfeeding, and electroconvulsive therapy within the 6 months before enrollment. Furthermore, social demographic data were collected using a self-designed electronic case report form, and a cognitive assessment was performed. A total of 9,940 participants participated in the study. The research protocol was approved by the ethical review board of Peking University Sixth Hospital (2021 Ethics Review No. 72).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eIndicator Measurements\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eOutcome Definitions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis study selected C-BCT, a simplified cognitive assessment tool developed by the Delphi method, which is highly correlated with MCCB (the gold standard for schizophrenia cognitive assessment, r = 0.76). C-BCT showed good internal consistency with a Cronbach’s α of 0.75,\u0026nbsp;The Intraclass Correlation Coefficient (ICC)\u0026nbsp;values for the four subtests and composite scaled score ranged from 0.62 to 0.76. It enables electronic testing within 15 minutes without requiring trained evaluators, offering an efficient, convenient, and scalable solution for large-scale studies.(\u003ca href=\"#_ENREF_20\" title=\"Ke, 2020 #52\"\u003e20\u003c/a\u003e,\u0026nbsp;\u003ca href=\"#_ENREF_21\" title=\"Ye, 2022 #24\"\u003e21\u003c/a\u003e)\u003c/p\u003e\n\u003cp\u003eC-BCT comprises four sub-tests: 1) Trail Making Test: respondents connect consecutive numbers placed randomly on an electronic screen. System auto-detects errors, alerts, and allows continuation. Scored by completion time. 2) Symbol Coding: respondents match numbers to symbols as quickly as possible within 90 seconds. Scored by total correct matches. 3) Continuous Performance Test: three sets of varying animal combinations flash continuously. Respondents click at two consecutive identical stimuli.\u0026nbsp;Scored by the number of correct and incorrect responses. 4) Digit Span: Voice-broadcast number sequences (2–9 digits, increasing length) play at set pace. Forward span: type in heard order; backward span: type in reverse. Scored according to the length of the remembered number. It is operated on tablets or smartphones and assesses the neurocognitive domains, including processing speed, sustained attention, verbal working memory, reasoning, and problem-solving. Cognitive function is graded as normal, mild, moderate, or severe based on standardized norms which were derived from 723 healthy participants in China.[20,21] C-BCT scores are obtained by converting the raw scores of each subtest to a mean of 10 with a standard deviation of 3 and then summing them for each participant. Normative Data was used to establish a cognitive score model to predict C-BCT scores, and then C-BCT scores were converted into standardized scores with a mean of 50 and standard deviation of 10 as T scores. Cognitive impairment is classified by the T score: A T score of 40 or higher is normal, 35-39 is mild, 25-29 is moderate, and 24 or lower is severe.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eRelated Outcome\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eSelf-reported employment status was used as the functional outcome, divided into employed, unemployed, and retired. However, since retirement could be due to either age or illness, only employment and unemployment were considered functional outcomes. Consequently, 441 retired samples were excluded from the analysis of function outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eInfluence Factors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eInformation on the date of onset, the date of first treatment, gender, age, education, and the presence of comorbidities, including diabetes mellitus and hypertension, were obtained through self-reports or medical records of the patient. The course of disease referred to the duration from onset to enrollment, and the duration of untreated was defined as the time from onset to first treatment. The level of education was divided into illiteracy, primary school, junior high school, high school (or technical secondary school), university (or junior college), and postgraduate or above. Regional factors were determined based on the geographical location of the enrolled hospitals, which were aligned with the seven geographical regions in China. Waist circumference, height, and weight were obtained by physical examinations, and body mass index (BMI) was calculated as weight divided by the square of height. According to the criteria for metabolic syndrome and obesity in China, a waist circumference of\u0026nbsp;≥\u0026nbsp;90 cm in men or\u0026nbsp;≥\u0026nbsp;85cm in women, and a BMI of\u0026nbsp;≥\u0026nbsp;28 were classified as abnormal.[22,23] Antipsychotic drugs were classified according to their pharmacological receptor action, whether they had partial dopamine 2 receptor (D2) excitability, partial serotonin 1A receptor (5-HT\u003csub\u003e1A\u003c/sub\u003e) excitability, and antagonism of serotonin 2A receptor (5-HT\u003csub\u003e2A\u003c/sub\u003e).\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eDescriptive statistics were initially conducted for cognitive assessment results and potentially related factors, including social-demographic attributes like gender, age, and education level, as well as health-related attributes like the course of the disease, time elapse to treatment, and comorbid hypertension. Subsequently, Chi-square tests were applied to examine the prevalence in different subgroups.\u003c/p\u003e\n\u003cp\u003eTo explore the factors in relation with cognitive function across the seven geographical regions, we employed OLS regression and GWR, conducted by ArcGIS 10.8 software. Firstly, the Moran Index was computed to evaluate the global spatial autocorrelation of the C-BCT scores. Next, all variables were standardized by Z-score to ensure comparability across different measures. OLS regression was then conducted to identify the key factors associated with cognitive performance, with the Variance Inflation Factor (VIF) test confirming no multicollinearity among the variables. Finally, GWR was applied, using the adaptive method as the space kernel function type and the Akaike information criterion (AIC) as the bandwidth method.\u003c/p\u003e\n\u003cp\u003eAntipsychotics were divided into different types according to the pharmacological receptors affected, and taken as a binary classification variable for the ordinal logistics analysis, with the degree of cognitive impairment as the dependent variable.\u0026nbsp;This analysis controlled for demographic factors, including age, gender, and educational level. Logistic regression analysis was performed for cognitive function and functional outcome indicators, controlling the confounding factors, including age, sex, and education level. This analysis, along with the descriptive statistics and the Chi-square test, was performed by SPSS 24.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eRole of the funding source\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe funders of the study played no role in study design, data collection, data analysis, data interpretation, writing of the report, or in the decision to submit it for publication.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003e\u003cstrong\u003eDescriptive Statistics\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eEligibility criteria were applied to identify participants by reviewing their compliance with the inclusion criteria, followed by screening for the availability of complete cognitive assessment data. Ultimately, 8309 qualified individuals were included for subsequent analysis (Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFig.\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Screening process and geographical distribution of participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe spatial distribution of participants was visualized based on the standard map (Review No. GS(2024)0650) obtained from the National Geographic Information Public Service Platform of China.\u003c/p\u003e\n\u003cp\u003eAmong the 8309 valid participants, 5132 were males (61.76%) and 3177 were females (38.24%), with an average age of 41.23 years (SD = 11.25). The average course of disease and years of education were 12.22 years (SD = 9.95) and 9.93 years (SD = 3.82), respectively. Regarding the duration of the disease before treatment, the average was 1.35 years (SD = 3.52) among all participants. The participants were recruited from seven geographical regions across China, with the cohort size as well as cognitive and non-cognitive impairment ratios for each region summarized in\u0026nbsp;Table 1. Our data reveal significant statistical differences in the proportion of cognitive impairment among various factors, including age, course of disease, duration of untreated, education, region, BMI, and waist circumference. Specifically, higher education level was correlated with a lower prevalence of cognitive impairment (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and patients with abnormal waist circumference (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and BMI (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05) had a lower prevalence of cognitive impairment compared to those within the normal range (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e1\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e.\u003cem\u003e\u0026nbsp;\u003c/em\u003eDescriptive statistics of the cohort\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive impairment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-cognitive impairment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3177 (38.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2378 (74.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e799 (25.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5132 (61.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3744 (72.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1388 (27.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (y), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e41.23 \u0026plusmn; 11.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e41.96 \u0026plusmn; 11.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e39.21 \u0026plusmn; 11.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCourse of disease (y), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12.22 \u0026plusmn; 9.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e12.54 \u0026plusmn; 10.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e11.35 \u0026plusmn; 9.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDuration of untreated (y), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1.40 \u0026plusmn; 3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1.22 \u0026plusmn; 3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (y), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e9.93 \u0026plusmn; 3.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e9.61 \u0026plusmn; 3.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e10.82 \u0026plusmn; 3.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"7\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eIlliteracy, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e198 (2.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e164 (82.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e34 (17.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003ePrimary school, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1292 (15.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1067 (82.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e225 (17.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eJunior high school, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3072 (36.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2339 (76.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e733 (23.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eHigh school (or technical secondary school), N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2126 (25.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1536 (72.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e590 (27.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eUniversity (or junior college), N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1555 (18.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e977 (62.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e578 (37.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003ePostgraduate or above, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e11 (0.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e6 (54.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e5 (45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegion, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"8\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNortheast China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1655 (19.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1418 (85.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e237 (14.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNorth China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1579 (19.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1033 (65.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e546 (34.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNorthwest China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e375 (4.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e255 (68.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e120 (32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eEast China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2187 (26.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1576 (72.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e611 (27.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eCentral China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1182 \u0026nbsp; \u0026nbsp; (14.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e788 (66.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e394 (33.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eSouthwest China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e931 (11.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e748 (80.34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e183 (19.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eSouth China\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e400 (4.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e304 (76.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e96 (24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI (kg/m\u003csup\u003e2\u003c/sup\u003e), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e23.81 \u0026plusmn; 5.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNormal, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7085 (85.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5258 (74.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1827 (25.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eAbnormal, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1148 \u0026nbsp; \u0026nbsp; (13.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e811 (70.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e337 (29.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWaist circumference (cm), Mean \u0026plusmn; SD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e82.37 \u0026plusmn; 21.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNormal, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e4856 (58.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e3709 (76.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e1147 \u0026nbsp; \u0026nbsp; (23.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eAbnormal, N (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e2829 (34.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e1964 (69.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e865 (30.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e459 (5.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e343 (74.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e116 (25.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7795 (93.81)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5744 (73.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2051 (26.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes, N (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e0.713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e443 (5.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e330 (74.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e113 (25.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 230px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e7810 (93.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e5756 (73.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e2054 (26.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cstrong\u003ePrevalence of Cognitive Impairment\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eAs the research indicates, according to C-BCT scores, 73.68% of stable schizophrenia patients in China exhibited varying degrees of cognitive impairment. 22.07%, 41.95%, and 9.65% of the patients had mild, moderate, and severe defects, respectively. The prevalence of cognitive impairment in each sub-test was as follows: 33.25% in the Trail Making Test, 54.3% in Symbol Coding, 52.87% in the Continuous Performance Test, and 64.11% in the Digit Span.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eFactors Associated with Cognitive Function\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThere were significant differences in cognitive function of patients with stable schizophrenia in different geographic regions. The C-BCT scores of stable schizophrenia patients in central China were the highest, while those in southwest China were the lowest (Figure 2). The Moran\u0026rsquo;s Index of C-BCT scores was 0.019, with a z-score of 91.085 and a p-value of 0.000, indicating statistically significant positive spatial autocorrelation of cognitive function.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFig. 2\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Spatial distribution of average C-BCT scores of stable schizophrenia patients across the seven geographical regions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the factors influencing cognitive function across the seven geographical regions, we employed OLS regression.\u0026nbsp;Table 2\u0026nbsp;summarizes the OLS regression results for factors associated with cognitive function. The Adjusted R\u003csup\u003e2\u003c/sup\u003e value reveals that the model accounts for over 22% of the variance in cognitive scores, suggesting a moderate explanatory power (Table 2). Cognitive function was positively correlated with years of education (coefficient = 0.295, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while negatively correlated with the course of disease (coefficient = -0.114, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and age (coefficient = -0.225, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Table 2). Patients with abnormal waist circumference had better cognitive performance (coefficient = 0.069, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas those diagnosed with diabetes showed lower cognitive scores (coefficient = -0.028, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Meanwhile, the VIF values for all variables were below 5.0, indicating that multicollinearity is not a concern in the analysis. Moreover, other factors, including gender, BMI comorbid hypertension, and duration of untreated, were eliminated from the model due to their non-significant influence on cognitive scores. In addition, the Koenker (BP) Statistic and Joint Wals Statistic were both statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), confirming that the overall model is significant and that the five factors exhibit spatial heterogeneity in their effects on cognitive function (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 2\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e. Results of the OLS regression model\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eIntercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e0.999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.295\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.316\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eCourse of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e-0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 61px;\"\u003e\n \u003cp\u003e-0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 536px;\"\u003e\n \u003cp\u003eDiagnostic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e0.222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 89px;\"\u003e\n \u003cp\u003e19518.903\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 203px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: CI: Confidence Interval.\u003c/p\u003e\n\u003cp\u003eWe further assessed the factors influencing cognitive function across different regions using GWR. The model explains approaching 40% of changes in cognitive scores, an improvement of nearly 18% in explanatory power compared to the OLS regression model (Table 3). Meanwhile, the AIC value decreased by 1656.591, indicating that the GWR model provides a substantially better fit and more effectively captures spatial variation in cognitive performance. Age exhibited a significantly negative association with cognitive function in Southwest China and a mildly negative relationship in Northeast China (Figure 3A). Education showed a stronger positive association with cognitive performance in South and Northwest China, though the association appeared weaker in Northeast China (Figure 3B). The course of disease was predominantly associated with cognition in the Northwest and Northeast regions (Figure 3C). Although waist circumference was significantly associated with cognitive performance, its influence was relatively weak, with minimal spatial differentiation in the coefficients (Figure 3D). Diabetes showed a substantial negative correlation in South China, while it exhibited a weak correlation in Northwest and Northeast China (Figure 3E).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 3\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e. Results of the GWR model\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 277px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStandard Deviation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMinimum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaximum\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eCourse of disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e-0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"top\" style=\"width: 553px;\"\u003e\n \u003cp\u003eDiagnostic information\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003eAIC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17862.312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 184px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFig. 3\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Impacts of five factors on cognitive function across the seven geographical regions\u003c/strong\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eEffects of Drugs on Cognitive Impairment\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eMedication usage was analyzed collectively for each subject. The most commonly by drugs were olanzapine, risperidone, and clozapine, accounting for 28.48% (N = 2366), 21.95% (N = 1824), and 21.88% (N = 1818), respectively (Table 4). Patients using 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonists (OR = 0.89, 95% CI: 0.81-0.97) and D2 receptor partial agonists (OR = 0.81, 95% CI: 0.73 -0.90) were negatively associated with the occurrence of cognitive impairment compared to those without them (Table 5). The FGA or SGA did not significantly associate with the degree of cognitive impairment compared with the combination of FGA and SGA. Similarly, the use of 5-HT\u003csub\u003e2A\u003c/sub\u003e receptor antagonists showed no significant association with cognitive impairment (\u003cem\u003ep\u003c/em\u003e ＞ 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 4\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e. Antipsychotic drug use in all participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrug\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eOlanzapine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e2366\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e28.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePaliperidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eClozapine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e21.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePerospirone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eRisperidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e21.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003ePerphenazine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAripiprazole\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e15.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eLurasidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eQuetiapine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eBlonanserin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eZiprasidone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e8.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eHaloperidol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 118px;\"\u003e\n \u003cp\u003eAmisulpride\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e613\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e7.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 5\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e. Association between types of antipsychotics and degrees of cognitive impairment\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"556\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLower\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpper\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eFGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eSGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.361\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eD2 receptor partial agonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e5-HT\u003csub\u003e2A\u003c/sub\u003e receptor antagonist\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 93px;\"\u003e\n \u003cp\u003e0.116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: FGA: first generation antipsychotics; SGA: second generation antipsychotics; D\u003csub\u003e2\u003c/sub\u003e receptor partial agonist includes Aripiprazole; 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonists include Aripiprazole, Quetiapine, Lurasidone, Perospirone, Ziprasidone; 5-HT\u003csub\u003e2A\u003c/sub\u003e receptor antagonists include Olanzapine, Risperidone, Clozapine, Aripiprazole, Quetiapine, Ziprasidone, Paliperidone, Perospirone, Lurasidone, Blonanserin.\u003c/p\u003e\n\u003ch2\u003e\u003cstrong\u003eImpact of Cognitive Impairment on Functional Outcomes in patients with stable schizophrenia\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003eThe logistic regression results reveal that cognitive impairment was significantly associated with unemployment (Table 6). After controlling for gender, age, and educational level, mild (OR = 1.95, 95% CI: 1.46-2.59), moderate (OR = 1.47, 95% CI: 1.10-1.98) and severe cognitive impairment(OR = 1.46, 95% CI: 1.10-1.93) were all significantly associated with unemployment compared to normal cognition(Table 6).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eTable 6\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e. The impact of cognitive impairment on employment outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEmployed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnemployed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRetired\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eMild\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e1506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003cp\u003e(1.46-2.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e382\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e2908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.47\u003c/p\u003e\n \u003cp\u003e(1.10-1.98)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003eSevere\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 101px;\"\u003e\n \u003cp\u003e699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 77px;\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 121px;\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003cp\u003e(1.10-1.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eAs the biggest single large-scale cross-sectional study of cognitive function in patients with stable schizophrenia, it enhances the representativeness of the results and provides valuable insight into the cognitive status of the Chinese population. Notably, this study marks the first large-scale generalized application of the C-BCT cognitive tool. Due to the simplicity and standardized electronic operation, C-BCT is well-suited for\u0026nbsp;brief cognitive screening and has proven effective in research involving patients with schizophrenia, which provides an affordable, reliable tool for large-scale screening of schizophrenia, especially in developing countries. This highlights its potential for broader application in future studies across various disease prevalence.\u003c/p\u003e\n\u003cp\u003eBuilding on previous studies that show over half of patients with schizophrenia experience cognitive impairment,[24] this study further quantified the high incidence of such impairment in stable schizophrenia, indicating that cognitive impairment persists even after symptoms have subsided. Among the four C-BCT sub-tests, the proportion of cognitive impairment was over 50% for both the Symbol Coding and Continuous Performance Test, with the Digit Span test showing the highest rate of impairment at 64.11%. These tests measure information processing speed and executive function, sustained and focused attention, and auditory and verbal working memory, respectively. This aligns with previous findings that individuals with schizophrenia have more severe impairments in processing speed, verbal memory, and working memory.[24] Additionally, other studies and reviews consistently emphasize that cognitive impairment in schizophrenia is particularly pronounced in processing speed and working memory.[8,25]\u003c/p\u003e\n\u003cp\u003eThe analysis of cognition-related factors revealed that age, disease course, and diabetes were significantly negatively correlated with cognitive function, while higher education level and waist circumference were positively correlated with cognitive function. Similar to previous findings, age and disease course are negatively correlated with cognitive function, while higher education level is positively correlated. Prior studies also showed that patients with cognitive impairment tend to have longer disease durations, higher disease severity scores, and higher medication doses.[26]Furthermore, subgroups with impaired cognitive function had fewer years of education than those with normal cognitive function.[27] For metabolism-related factors, patients with diabetes had worse cognitive performance, while those with abnormal waist circumference had better cognitive performance. A meta-analysis supports that schizophrenia patients with diabetes experience more severe cognitive impairment.)[19] Some studies suggest that obesity is not associated with cognition,[17 while others indicate that schizophrenia with obesity has poorer cognitive performance.[19] Another meta-analysis suggests that high waist circumference itself could be a risk factor for cognitive impairment and dementia.[28]Conversely, this study showed that individuals with abnormal waist circumference demonstrated better cognitive performance. This may be influenced by psychiatric medications, which often affect metabolism and waist circumference, as well as the confounding effects of efficacy and disease severity. A study has shown that the response to clozapine is correlated with waist circumference, with clozapine-resistant groups exhibiting smaller waist circumference.[29] It is hypothesized that the increase in waist circumference may be more pronounced in patients with a better drug response. These patients tend to have a better treatment response and less cognitive impairment, which may explain why the group with abnormal waist circumference has less cognitive impairment.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eInterestingly, we observed significant spatial heterogeneity in cognitive function across the seven geographical regions, an observation rarely reported in previous studies. This finding adds a new dimension and enhances the resolution of our analysis. The possible contributing factors may be: 1) Age differences. Participants from Central China had the youngest average age, which may account for their highest cognitive performance, as cognitive function typically declines with age. 2) Economic disparity. Research indicates that economic recession and poverty may be associated with cognitive decline. [30,31]The relatively underdeveloped economy in Southwest China likely contributed to the lowest cognitive performance observed in this region. 3) Prevalence of Mandarin. Mandarin proficiency varies across China, with the Western region showing lower prevalence than other regions. [32]Since the C-BCT cognitive assessment tools used in our study were administered in Mandarin, lower Mandarin proficiency in the Western region may have led to difficulties in fully understanding the C-BCT cognitive assessment tools, resulting in lower cognitive scores.\u003c/p\u003e\n\u003cp\u003eThe analysis of antipsychotic factors, classified by receptor action, revealed that subgroups using 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonists or D2 receptor partial agonists had less cognitive impairment. This may be related to the role of the 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor in learning and memory. [33,34]Aripiprazole and quetiapine (with a partial 5-HT\u003csub\u003e1A\u003c/sub\u003e agonist) can also improve cognitive function.[13] This study also innovatively investigated whether combination therapy is associated with cognitive function and observed no significant difference in cognitive function between patients treated with the combination drug and those treated with monotherapy, whether FAG or SAG, suggesting that combination therapy can be used in clinical practice when necessary, without undue concern about exacerbating cognitive impairment. However, these findings warrant further investigations. As a real-world cross-sectional study, the reliability of the results may be affected by the fact that most patients (97%) used combination therapy.\u003c/p\u003e\n\u003cp\u003eAmong stable schizophrenia patients, cognitive impairment has been shown to be associated with unemployment, irrespective of the severity of the impairment. This is consistent with existing research concluding cognitive function as a predictor of employment outcomes. Interestingly, patients with mild cognitive impairment showed a higher odds ratio (OR) for unemployment compared to those with moderate or severe impairment, which can be attributed to a combination of higher expectations, lack of support, the transitional nature of Mild Cognitive Impairment, and insufficient workplace accommodations. Given that cognitive remediation training can enhance functional outcomes, [35]including employment,[36] it is crucial to prioritize such interventions to improve the prognosis of schizophrenia patients, particularly those with mild cognitive impairment.\u003c/p\u003e\n\u003cp\u003eDue to the impacts of education levels and disease courses on cognitive function, it is essential to prioritize educational programs and early interventions to mitigate cognitive impairment by providing educational support and shortening illness duration. The study also validated the relationship between cognitive function and functional outcomes, emphasizing the importance of addressing cognitive impairment in clinical practice and giving interventions to improve patients’ long-term quality of life.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMeanwhile, this study also has some limitations. As a cross-sectional study, it cannot establish causality. For example, while the results indicate that education may act as a protective factor for cognitive function, it is also possible that cognitive impairment in patients with schizophrenia prevents them from completing their education, leading to lower educational attainment. Thus, longitudinal studies are needed to explore these relationships more thoroughly. In addition, our study of employment outcomes is brief, and future research should include more specific measures such as hours worked and income earned, etc. Patients' disease-related information—such as the date of onset, the date of first treatment, and the presence of comorbidities—was obtained through self-reporting, a method inherently prone to recall bias that may introduce inaccuracies. Although differences in cognitive function among patients with schizophrenia have been observed across different regions, the underlying causes remain unclear. Future research could explore factors such as economic status, education, healthcare, diet, and culture to gain a deeper understanding.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study represents the first large-scale cross-sectional analysis of cognitive function in patients with stable schizophrenia and the initial generalized application of C-BCT cognitive tools. Among 8309 patients in China, 73.68% (N = 6122) exhibited cognitive impairment. Cognitive function was significantly negatively correlated with age, disease course, and diabetes, while positively correlated with higher education level and waist circumference. These correlations showed significant spatial heterogeneity across seven geographical regions. Additionally, the types of antipsychotic drugs were related to cognitive impairment, with subgroups using 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonists or D2 receptor partial agonists showing less impairment. Mild, moderate, and severe cognitive impairments are all significantly associated with unemployment outcomes. These findings emphasize the critical role of cognitive function in patients with schizophrenia, highlighting the need for increased attention, awareness, and interventions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eINTERPRETATION\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEarly awareness and interventions are critical for combating the high prevalence of cognitive impairment to improve the quality of life and employment in stable schizophrenia. The spatial heterogeneity also points out the need for addressing economic disparities and more healthcare support.\u003c/p\u003e\n\u003cp\u003eCONTRIBUTORS\u003c/p\u003e\n\u003cp\u003eChuan Shi, Xin Yu, and Gong Chen conceived the study. Yuanyuan Zhou, Chuan Shi, and Xin Yu collected data. Yuanyuan Zhou, Bin Guo, Qiyao Yang, and Zhiquan Li performed formal data analysis. Yuanyuan Zhou and Yumei Cai formulated the initial version of the manuscript. Chi Zhang, Zhiquan Li, Qiyao Yang, Jun Cai, and Vilhelm A. Bohr reviewed and revised the manuscript. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003eAVAILABILITY OF DATA AND MATERIALS\u003c/p\u003e\n\u003cp\u003eThe majority of the information collected or analyzed during this study is presented and discussed in this article. The Peking University Institute of Mental Health\u0026rsquo;s Ethics Committee reviewed and approved all studies involving participants. The patients/participants gave their written informed consent to take part in this study.\u003c/p\u003e\n\u003cp\u003eDECLARATION OF INTERESTS\u003c/p\u003e\n\u003cp\u003eWe declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJauhar S, Johnstone M, McKenna PJ. Schizophrenia. Lancet. 2022;399(10323):473-486.\u003c/li\u003e\n\u003cli\u003eGBD 2019 Mental Disorders Collaborators. Global, regional, and national burden of 12 mental disorders in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Psychiatry. 2022;9(2):137-150.\u003c/li\u003e\n\u003cli\u003eHalverson TF, Orleans-Pobee M, Merritt C, Sheeran P, Fett AK, Penn DL. Pathways to functional outcomes in schizophrenia spectrum disorders: Meta-analysis of social cognitive and neurocognitive predictors. Neurosci Biobehav Rev. 2019;105:212-19.\u003c/li\u003e\n\u003cli\u003ePalmer BW, Heaton RK, Paulsen JS, et al. Is it possible to be schizophrenic yet neuropsychologically normal? Neuropsychology. 1997;11(3):437-446.\u003c/li\u003e\n\u003cli\u003eReichenberg A, Harvey PD, Bowie CR, et al. Neuropsychological function and dysfunction in schizophrenia and psychotic affective disorders. Schizophr Bull. 2009;35(5):1022-29.\u003c/li\u003e\n\u003cli\u003eChen S, Liu Y, Liu D, Zhang G, Wu X. The difference of social cognitive and neurocognitive performance between patients with schizophrenia at different stages and influencing factors. Schizophr Res Cogn. 2021;24:100195.\u003c/li\u003e\n\u003cli\u003eGold S, Arndt S, Nopoulos P, O\u0026apos;Leary DS, Andreasen NC. Longitudinal study of cognitive function in first-episode and recent-onset schizophrenia. Am J Psychiatry. 1999;156(9):1342-8.\u003c/li\u003e\n\u003cli\u003eKern RS, Gold JM, Dickinson D, et al. The MCCB impairment profile for schizophrenia outpatients: results from the MATRICS psychometric and standardization study. Schizophr Res. 2011;126(1-3):124-131.\u003c/li\u003e\n\u003cli\u003eda Motta C, Pato MT, Barreto Carvalho C, Castilho P. The neurocognitive and functional profile of schizophrenia in a genetically homogenous European sample. Psychiatry Res. 2021;304:114140.\u003c/li\u003e\n\u003cli\u003eZhao N, Wang XH, Kang CY, et al. Sex differences in association between cognitive impairment and clinical correlates in Chinese patients with first-episode drug-na\u0026iuml;ve schizophrenia. Ann Gen Psychiatry. 2021;20(1):26.\u003c/li\u003e\n\u003cli\u003eKrysta K, Murawiec S, Klasik A, Wiglusz MS, Krupka-Matuszczyk I. Sex-specific differences in cognitive functioning among schizophrenic patients. Psychiatr Danub. 2013;25 Suppl 2:S244-46.\u003c/li\u003e\n\u003cli\u003eCao X, Chen S, Xu H, Wang Q, Zhang Y, Xie S. Global functioning, cognitive function, psychopathological symptoms in untreated patients with first-episode schizophrenia: A cross-sectional study. Psychiatry Res. 2022;313:114616.\u003c/li\u003e\n\u003cli\u003eMacKenzie NE, Kowalchuk C, Agarwal SM, et al. Antipsychotics, Metabolic Adverse Effects, and Cognitive Function in Schizophrenia. Front Psychiatry. 2018;9:622.\u003c/li\u003e\n\u003cli\u003eWoodward ND, Purdon SE, Meltzer HY, Zald DH. A meta-analysis of neuropsychological change to clozapine, olanzapine, quetiapine, and risperidone in schizophrenia. International Journal of Neuropsychopharmacology. 2005;8(3):457-72.\u003c/li\u003e\n\u003cli\u003eBaldez DP, Biazus TB, Rabelo-da-Ponte FD, et al. The effect of antipsychotics on the cognitive performance of individuals with psychotic disorders: Network meta-analyses of randomized controlled trials. Neuroscience \u0026amp; Biobehavioral Reviews. 2021;126:265-275.\u003c/li\u003e\n\u003cli\u003eAkdede BB, Alptekin K, Bora E. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychological Medicine. 2017;47(6):1030-40.\u003c/li\u003e\n\u003cli\u003eHagi K, Nosaka T, Dickinson D, et al. Association Between Cardiovascular Risk Factors and Cognitive Impairment in People With Schizophrenia: A Systematic Review and Meta-analysis. JAMA Psychiatry. 2021;78(5):510-18.\u003c/li\u003e\n\u003cli\u003eMeyer JM, Nasrallah HA, McEvoy JP, et al. The Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Schizophrenia Trial: Clinical comparison of subgroups with and without the metabolic syndrome. Schizophrenia Research. 2005;80(1):9-18.\u003c/li\u003e\n\u003cli\u003eBora E, Akdede BB, Alptekin K. The relationship between cognitive impairment in schizophrenia and metabolic syndrome: a systematic review and meta-analysis. Psychol Med. 2017;47(6):1030-40.\u003c/li\u003e\n\u003cli\u003eKe M, Xin Y, Chunbo L, Chuan S. A Delphi study of the Brief Cognitive Assessment for Schizophrenia. Chinese Mental Health Journal. 2020;34(9):736-40.\u003c/li\u003e\n\u003cli\u003eYe S, Xie M, Yu X, et al. The Chinese Brief Cognitive Test: Normative Data Stratified by Gender, Age and Education. Front Psychiatry. 2022;13:933642.\u003c/li\u003e\n\u003cli\u003ePan XF, Wang L, Pan A. Epidemiology and determinants of obesity in China. Lancet Diabetes Endocrinol. 2021;9(6):373-92.\u003c/li\u003e\n\u003cli\u003eJunren Z, Runlin G, Shuiping Z, Guoping L, Dong Z, Jianjun L. Guidelines for the Prevention and Treatment of Dyslipidemia in Adults in China (2016 Revision). Chinese Circulation J. 2016;31(10):937-392.\u003c/li\u003e\n\u003cli\u003eGebreegziabhere Y, Habatmu K, Mihretu A, Cella M, Alem A. Cognitive impairment in people with schizophrenia: an umbrella review. Eur Arch Psychiatry Clin Neurosci. 2022;272(7):1139-55.\u003c/li\u003e\n\u003cli\u003eMcCleery A, Nuechterlein KH. Cognitive impairment in psychotic illness: prevalence, profile of impairment, developmental course, and treatment considerations\u2029. Dialogues Clin Neurosci. 2019;21(3):239-48.\u003c/li\u003e\n\u003cli\u003eOrtiz-Gil J, Pomarol-Clotet E, Salvador R, et al. Neural correlates of cognitive impairment in schizophrenia. Br J Psychiatry. 2011;199(3):202-10.\u003c/li\u003e\n\u003cli\u003eCarruthers SP, Van Rheenen TE, Karantonis JA, Rossell SL. Characterising Demographic, Clinical and Functional Features of Cognitive Subgroups in Schizophrenia Spectrum Disorders: A Systematic Review. Neuropsychology Rev. 2022;32(4):807-27.\u003c/li\u003e\n\u003cli\u003eTang X, Zhao W, Lu M, et al. Relationship between Central Obesity and the incidence of Cognitive Impairment and Dementia from Cohort Studies Involving 5,060,687 Participants. Neurosci Biobehav Rev. 2021;130:301-313.\u003c/li\u003e\n\u003cli\u003eH\u0026ouml;nig G, Daray FM, Rodante D, Drucaroff L, Guti\u0026eacute;rrez ML, Lenze M, Garc\u0026iacute;a Bournissen F, Wikinski S. Body mass index, waist circumference, insulin, and leptin plasma levels differentiate between clozapine-responsive and clozapine-resistant schizophrenia. J Psychopharmacol. 2023;37(10):1023-1029.\u003c/li\u003e\n\u003cli\u003eLeist AK, Hessel P, Avendano M. Do economic recessions during early and mid-adulthood influence cognitive function in older age? J Epidemiol Community Health. 2014;68(2):151-8.\u003c/li\u003e\n\u003cli\u003eMani A, Mullainathan S, Shafir E, Zhao J. Poverty impedes cognitive function. Science. 2013;341(6149):976-80.\u003c/li\u003e\n\u003cli\u003eWeiqi Y. Current Situation and Development of National Putonghua Proficiency. Applied Linguistics. 2018;(2):99-07.\u003c/li\u003e\n\u003cli\u003eGlikmann-Johnston Y, Saling MM, Reutens DC, Stout JC. Hippocampal 5-HT1A Receptor and Spatial Learning and Memory. Front Pharmacol. 2015;6:289.\u003c/li\u003e\n\u003cli\u003eOgren SO, Eriksson TM, Elvander-Tottie E, et al. The role of 5-HT(1A) receptors in learning and memory. Behav Brain Res. 2008;195(1):54-77.\u003c/li\u003e\n\u003cli\u003eMedalia A, Saperstein AM. Does cognitive remediation for schizophrenia improve functional outcomes? Curr Opin Psychiatry. 2013;26(2):151-57.\u003c/li\u003e\n\u003cli\u003eMcGurk SR, Mueser KT, Xie H, et al. Cognitive Enhancement Treatment for People With Mental Illness Who Do Not Respond to Supported Employment: A Randomized Controlled Trial. Am J Psychiatry. 2015;172(9):852-61.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Stable Schizophrenia, Cognitive Impairment, Chinese Brief Cognitive Test","lastPublishedDoi":"10.21203/rs.3.rs-8366736/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8366736/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e This study aims to investigate the prevalence of cognitive impairment in patients with stable schizophrenia, analyze factors influencing its occurrence, and examine its impact on functional outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eThis study includes a sample of 8309 participants diagnosed with stable schizophrenia in China between March and November 2022. Social-demographic and health-related data of the participants were collected. Their cognitive function was assessed by the Chinese Brief Cognitive Test (C-BCT). To evaluate factors associated with cognitive function, Ordinary Least Squares (OLS) regression and Geographically Weighted Regression (GWR) were applied. Logistic regression was further employed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eThe proportion of patients with cognitive impairment (defined as cognitive function 1 standard deviation below the population mean on the C-BCT) was 73.68% (N = 6122) in China. Age, disease course, and diabetes were significantly negatively associated with cognitive function, whereas education and waist circumference showed significant positive associations, with significant spatial heterogeneity across the seven geographical regions. Patients treated with 5-HT\u003csub\u003e1A\u003c/sub\u003e receptor partial agonists (OR = 0.89, 95% CI: 0.81-0.97) and D2 receptor partial agonists (OR = 0.81, 95% CI: 0.73-0.90) were negatively associated with the occurrence of cognitive impairment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eThis study represents the first large-scale cross-sectional analysis of cognitive function in patients with stable schizophrenia and the initial generalized application of C-BCT cognitive tools. The study found for the first time that the subgroups using 5-HT1A receptor partial agonists or D2 receptor partial agonists showing less impairment. In the future, attention, awareness and interventions should be researched in depth.\u003c/p\u003e","manuscriptTitle":"Cognitive Impairment in Stable Schizophrenia: Insights from a Large-Scale Chinese Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-13 12:26:31","doi":"10.21203/rs.3.rs-8366736/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2026-04-16T09:48:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-04-06T13:30:38+00:00","index":4,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-03-22T11:31:24+00:00","index":3,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-11T07:24:56+00:00","index":4,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-03-08T00:29:20+00:00","index":3,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-25T12:49:14+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-02-11T17:12:35+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-10T08:15:07+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-10T01:38:59+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-02-10T01:37:11+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-04T15:26:33+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-04T15:07:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Psychiatry","date":"2025-12-19T01:18:21+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2025-12-16T13:57:02+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"mp","sideBox":"Learn more about [Molecular Psychiatry](http://www.nature.com/mp/)","snPcode":"41380","submissionUrl":"https://mts-mp.nature.com/cgi-bin/main.plex","title":"Molecular Psychiatry","twitterHandle":"@molpsychiatry","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"d2cae4b0-6618-4bf8-9a5e-54fce791b4f0","owner":[],"postedDate":"February 13th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":62623678,"name":"Biological sciences/Psychology"},{"id":62623679,"name":"Health sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-04-16T09:52:41+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-13 12:26:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8366736","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8366736","identity":"rs-8366736","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}