Comparative Effectiveness of Metformin versus GLP-1 Receptor Agonists in Treating Antipsychotic-Induced Metabolic Disturbances: A Systematic Review and Network Meta-Analysis

Comparative Effectiveness of Metformin versus GLP-1 Receptor Agonists in Treating Antipsychotic-Induced Metabolic Disturbances: A Systematic Review and Network Meta-Analysis | 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 Research Article Comparative Effectiveness of Metformin versus GLP-1 Receptor Agonists in Treating Antipsychotic-Induced Metabolic Disturbances: A Systematic Review and Network Meta-Analysis Ye-xin Chen, Qian-wen Yang, Mao-xuan Lin, Mo-han Sun, Yi-yu Dong, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9317100/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Objective: The management of antipsychotic-induced metabolic disturbances (AIMD) represents a significant challenge in psychiatric clinical practice. Although metformin is widely used to improve AIMD, the role of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in metabolic regulation has gained increasing attention. This study aims to compare the efficacy of metformin and different GLP-1 RAs in improving multidimensional metabolic indicators and psychiatric symptoms in AIMD patients through a systematic review and network meta-analysis. Methods: Randomized controlled trials (RCTs) published up until December 1, 2025, were identified by searching PubMed, Embase, Cochrane Library, and Web of Science databases. Studies that included patients receiving metformin or GLP-1 RAs treatment for at least 12 weeks, while continuously using antipsychotic medications, were included. The Cochrane Risk of Bias 2.0 tool was used to assess the quality of the studies. A random-effects network meta-analysis was performed using Stata 17.0 MP within the frequentist framework. Intervention rankings were determined by calculating the surface under the cumulative ranking curve (SUCRA). Univariate network meta-regression was applied to explore the impact of study-level covariates on treatment efficacy. Evidence quality was rated based on the CINeMA framework. Results: A total of 29 RCTs (1,761 patients) were included. Semaglutide demonstrated the most significant effect in reducing body mass index (BMI) (MD = -3.55, 95% CI: -4.27 to -2.84). It also showed the best results in reducing waist circumference (WC) (MD = -6.34, 95% CI: -8.17 to -4.51). Moreover, semaglutide was significantly superior to other interventions in controlling glycated hemoglobin A1c (HbA1c) (MD = -0.44, 95% CI: -0.53 to -0.35) and fasting blood glucose (FBG) (MD = -0.53, 95% CI: -0.88 to -0.18). Additionally, metformin demonstrated a significant advantage over placebo in improving psychiatric symptom scores (SMD = -0.35, 95% CI: -0.61 to -0.09), and showed unique benefits in regulating lipid metabolism markers such as total cholesterol and triglycerides. Conclusion: Different medications exhibit distinct advantages in managing AIMD across various metabolic indicators. GLP-1 RAs, particularly semaglutide, demonstrate remarkable efficacy in weight loss and glycemic control, while metformin excels in lipid regulation and psychiatric symptom improvement. Clinical decisions should be individualized based on the patient's specific metabolic abnormalities, with a comprehensive consideration of the dual impact of medications on both metabolic and psychiatric symptoms to achieve optimal overall health. Metformin GLP-1 Receptor Agonists Antipsychotic-Induced Metabolic Disturbances Systematic Review Network Meta-Analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Mental disorders, particularly severe psychiatric conditions such as schizophrenia, impose asignificant global burden on public health. These conditions not only profoundly affect patients' cognition, emotions, and daily functioning, but also lead to a marked reduction in overall life expectancy( 1 ).A study has shown that individuals with schizophrenia face a higher risk of mortality compared to the general population, with cardiovascular diseases and metabolic complications being major non-psychiatric causes of death. The overall life expectancy of these patients can be shortened by 10 to 20 years, a disparity largely attributed to the accumulation of metabolic risk factors and increased cardiovascular burden( 2 ). As societal awareness of mental health issues continues to grow, the use of antipsychotic medications has become more widespread and is now a first-line treatment for severe psychiatric disorders such as schizophrenia and bipolar disorder( 3 ). In clinical practice, particularly with second-generation antipsychotic drugs (SGAs), while these medications effectively improve psychiatric symptoms and reduce relapse rates, they are also associated with significant metabolic side effects. These adverse effects include weight gain, lipid metabolism abnormalities, insulin resistance, impaired glucose tolerance, and even the development of type 2 diabetes and hypertension( 4 ), collectively referred to as Antipsychotic-Induced Metabolic Disturbances (AIMD). A meta-analysis involving data from 35,007 participants demonstrated a significant association between the long-term use of medications such as chlorpromazine, clozapine, and olanzapine, and various metabolic disorders, including weight gain and deterioration in blood glucose and lipid parameters( 5 , 6 ). An observational cohort study of 767 patients with schizophrenia found that clozapine and olanzapine significantly increased BMI and led to several adverse metabolic changes. Furthermore, metabolic risks varied across different SGAs, suggesting the need for clinical monitoring of these metabolic side effects during long-term treatment. Various intervention strategies have been implemented in clinical practice to address AIMD. Early studies and clinical guidelines have recommended metformin as a first-line drug to prevent or alleviate these metabolic side effects( 7 ). Several randomized controlled trials (RCT) and comprehensive meta-analyses have shown that metformin, as an adjunctive treatment, significantly reduces weight gain and lowers BMI, with additional improvements in metabolic parameters such as blood lipids( 8 ). With the widespread use and successful application of GLP-1 receptor agonists (GLP-1RAs) in the treatment of diabetes and obesity, their potential efficacy in addressing AIMD has gradually garnered attention( 9 ). Existing systematic reviews and clinical trials suggest that GLP-1 RAs can improve weight, blood glucose, and other metabolic parameters in patients with AIMD, with good tolerability( 10 ). However, the current studies are mostly small-sample, short-term follow-up trials, and there is a lack of high-quality meta-analyses that provide a comprehensive summary of broader outcome measures, such as lipid profiles, blood pressure, and psychiatric symptom( 11 ). In conclusion, although research has explored the therapeutic effects of metformin and GLP-1 RAs on AIMD, comprehensive evidence regarding broader metabolic indicators and psychiatric symptoms is still urgently needed. Furthermore, there is a lack of high-quality network meta-analyses that directly compare the two most common AIMD medications, metformin and GLP-1 RAs, regarding their effects on various metabolic outcomes. Therefore, this study aims to conduct a network meta-analysis to systematically integrate the multidimensional efficacy of metformin and GLP-1 RAs in the treatment of AIMD, providing more comprehensive evidence for clinical practice. 2 Methods 2.1 Materials and Methods This network meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension statement for network meta-analyses. Due to the lack of direct comparisons between metformin and various GLP-1 RAs in AIMD patients, indirect comparisons were employed to predict the probability rankings of different treatment regimens regarding their metabolic efficacy and safety. To ensure transparency, reliability, and novelty, the study protocol was registered in the Prospective Register of Systematic Reviews(12, 13) (CRD420251159491). 2.2 Data Sources and Search Strategy A systematic search was conducted in the PubMed, Embase, Cochrane Library, and Web of Science databases to identify randomized controlled trials evaluating the effects of metformin and GLP-1 RAs in the treatment of AIMD from the inception of each database up to December 1, 2025. The search strategy combined free-text and subject terms, with a restriction to English-language articles. Five reviewers (Chen YX, Yang QW, Sun MX, Dong YY, Zhang L) independently screened the titles and abstracts in duplicate. Discrepancies were resolved by consulting a sixth reviewer (Lin MX). Additionally, the reference lists of the included articles and relevant systematic reviews were screened to identify potentially eligible studies. A full list of the search strategy is available in Table S1 . 2.3 Selection Criteria Randomized clinical trials with the following inclusion criteria were included: (1) patients receiving metformin or GLP-1 RA interventions; (2) patients undergoing at least one antipsychotic medication regimen; (3) a minimum follow-up duration of 12 weeks. Studies had to report at least one of the following outcome measures: (1) BMI, defined as weight (kg) divided by height (m) squared, consistently reported in units of kg/m²; (2) waist circumference (WC), measured at the midpoint between the lower edge of the rib cage and the upper edge of the iliac crest using a soft tape measure, consistently reported in centimeters (cm); (3) fasting blood glucose (FBG), defined as venous plasma glucose concentration measured after at least 8 hours of fasting, consistently reported in mmol/L (with conversion of mg/dL to mmol/L using the factor 1 mmol/L = 18 mg/dL); (4) Glycated Hemoglobin A1c (HbA1c), representing the average blood glucose level over the past 2–3 months, consistently reported in percentage (%); (5) systolic blood pressure (SBP), defined as the arterial pressure during the contraction of the heart, consistently reported in mmHg; (6) diastolic blood pressure (DBP), defined as the arterial pressure during the relaxation phase of the heart, consistently reported in mmHg; (7) total cholesterol (TC), defined as the total cholesterol concentration from all lipoproteins in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L = 38.67 mg/dL); (8) triglycerides (TG), defined as the concentration of triglycerides in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L = 88.57 mg/dL); (9) high-density lipoprotein cholesterol (HDL-C), defined as the concentration of high-density lipoprotein cholesterol in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L = 38.67 mg/dL); (10) low-density lipoprotein cholesterol (LDL-C) defined as the concentration of low-density lipoprotein cholesterol in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L = 38.67 mg/dL); (11) psychiatric rating scores, including the total score or key subscale scores from validated, standardized scales used to assess the severity of mental illness symptoms or overall functioning. Exclusion criteria included: (1) reviews, letters, conference abstracts, or case reports; (2) RCTs with unclear outcome measures; (3) RCTs based on different stages of the same group of patients; (4) cross-over design studies; (5) non-inferiority trials comparing metformin/GLP-1 RAs with other non-placebo medications. Before inclusion, RCTs were screened by title and abstract. All included RCTs were double-checked by two reviewers to ensure that the data were from the most recent publications. 2.4 Screening Process and Data Extraction The retrieved database records were imported into EndNote 20.4.1 (Clarivate Analytics, Philadelphia, PA, USA) to remove duplicates, and the results were combined with those from other sources. The screening process was conducted in three stages. First, three reviewers (Yang QW, Dong YY, and Zhang L) independently selected articles based on titles, including any uncertain entries. Second, all articles selected in the first stage were reviewed in detail, with discrepancies resolved through discussions among the reviewers, and a fourth reviewer (Chen YX) was consulted when necessary. Third, the full texts of articles that met the inclusion and exclusion criteria based on their titles and abstracts were further examined. For each eligible study, a pre-designed form was used to independently extract the following information: study characteristics (publication year, country, treatment duration), population (age, gender, sample size), interventions (name and dosage), and outcomes. Data extraction was performed by two independent reviewers (Chen YX and Yang QW), with subsequent verification and arbitration by a third reviewer (Lin MX)(14, 15). The changes were measured from baseline in outcomes including BMI, WC, BP, FBG, HbA1c, HDL-C, LDL-C, TC, TG, and psychiatric rating scores. Given the significant heterogeneity in psychological assessment tools across studies, and the frequent use of multiple scales (e.g., Positive and Negative Syndrome Scale, PANSS; Brief Psychiatric Rating Scale, BPRS; Clinical Global Impression, CGI; Young Mania Rating Scale, YMRS) within the same study, we prioritized extracting and combining the total scores from standardized scales specifically focused on assessing core schizophrenia symptoms. The primary psychiatric symptom data used in the network meta-analysis were derived from the PANSS total score, MATRICS Consensus Cognitive Battery (MCCB) composite score, and BPRS total score. These outcomes were primarily extracted as mean changes and standard deviations (SD) from baseline to follow-up. When only baseline and endpoint data were reported, the mean change and SD were calculated using the following formula(16, 17). If the relevant correlation coefficient (r) was not reported and could not be inferred, we assumed r = 0.5 to calculate the values and assess the robustness of the conclusions(18). 2.5 Quality Assessment The Cochrane Risk of Bias Tool (version 2.0) (19)was used to assess the risk of bias in the included trials across five domains: randomization, deviations from the intended interventions, missing data, outcome measurement, and selection of reported results. If all domains had a low risk of bias, the overall risk for each trial was considered "low." If any domain had a high risk of bias, the overall risk was considered "high." In other cases, the risk of bias was classified as "some concerns." The risk of bias assessment was performed independently by two reviewers, and any discrepancies were resolved through consensus. 2.6 Statistical Analysis A network meta-analysis was conducted using Stata 17.0 MP. For continuous outcome variables with consistent units, the mean difference (MD) and its 95% confidence interval (CI) were used. For psychiatric symptom scores assessed with different ratings, the standardized mean difference (SMD) and its 95% CI were calculated. The primary analysis was performed using a random-effects model under the consistency assumption, with the between-study variance (τ²) estimated using the Restricted Maximum Likelihood (REML) method(20). If a closed-loop structure existed in the network, global inconsistency tests were used to assess consistency, along with local inconsistency tests using the node-splitting method(21). A p-value < 0.1 was considered indicative of potential inconsistency. The inconsistency factor (IF) was used to evaluate the consistency of closed loops (on the log scale of effects). If the 95% CI of IF included 0, no statistical evidence of inconsistency between direct and indirect evidence was found. If no closed-loop inconsistency was present, the consistency model was used for analysis. A network graph was created to visualize the geometric structure of the network, where node size was proportional to the total sample size of each treatment, and the thickness of the connecting lines represented the number of studies comparing two interventions. To rank the interventions, multiple ranking metrics were applied, including the surface under the cumulative ranking curve (SUCRA)(22), the probability of being the best treatment (PreBest), and the mean rank(23), to enhance the robustness and interpretability of the results(24). Publication bias and small sample effects were assessed using a comparison-adjusted funnel plot (for networks with more than 10 studies)(23). Sensitivity analysis was performed using the leave-one-out method, by excluding each individual study one at a time, and comparing the direction and magnitude of the combined effects from the random-effects consistency model(25, 26). Further, a univariate network meta-regression was conducted to explore the impact of study-level covariates on treatment effects. Covariates such as intervention time, study location (country/region), mean age of participants, type of mental illness, and whether structured lifestyle interventions were included were analyzed. Since lifestyle interventions are a common part of metabolic management and many studies encouraged patients to modify their lifestyle alongside trial medications, this factor was not distinguished in the network meta-analysis. To assess its impact, we performed a regression analysis based on whether the study included structured lifestyle interventions, to explore its potential moderating effect on treatment outcomes. The regression coefficients, 95% CIs, and Wald test p-values were reported. A p-value < 0.05 was considered to provide statistical evidence of a moderating effect. 2.7 GRADE Grading The quality of the network meta-analysis results was evaluated using the GRADE framework(27) and the CINeMA (Confidence in Network Meta-analysis) tool(28). Randomized controlled trials were initially rated as "high certainty." The certainty of evidence was determined by assessing six domains: study-level bias, indirectness, imprecision, heterogeneity, inconsistency, and publication bias/small sample effects(29, 30). Study-level bias was assessed using the RoB 2.0 tool for each domain, and the bias risk was weighted according to its contribution to the network estimate using the CINeMA contribution matrix. Indirectness was assessed based on assumptions of transitivity and exchangeability, with potential effect modifiers predefined to compare the consistency between direct and indirect evidence for population, intervention, control, and outcome measurements. Imprecision was assessed by comparing the effect estimates' 95% CI with the predefined minimal clinical important difference (MID). For continuous outcomes, we defined a SMD of 0.5 as the MID threshold and evaluated whether the 95% CI for effect estimates crossed the null value and this clinical threshold. Heterogeneity was assessed based onτ 2 estimated by the random-effects model and the position of the prediction interval relative to the MID. For networks with closed loops, inconsistency was evaluated using CINeMA's built-in methods (node-splitting or design-treatment interaction models) to assess the consistency between direct and indirect evidence. Publication bias was assessed based on trial registration and grey literature searches, and small sample effects were evaluated using comparison-adjusted funnel plots. Each domain was graded as "no concerns," "some concerns," or "serious concerns," and the evidence certainty was downgraded according to the GRADE principles. If concerns were present, the level was downgraded by one level; if serious concerns were found, the level was downgraded by two levels. The final evidence certainty was categorized as high, moderate, low, or very low. 3 Result 3.1 Characteristics of Included Trials In the initial literature search, a total of 9,588 records were retrieved from the databases (8,290 after removing duplicates). After screening the abstracts to exclude duplicates and irrelevant articles, 212 studies were deemed eligible for full-text review. Ultimately, 29 studies met our inclusion criteria( 31 – 59 ). (Fig. 1 ) A total of 1,761 patients were enrolled in the trials, receiving four interventions: metformin, semaglutide, liraglutide, and exenatide. The included studies were published between 2006 and 2025, with the majority conducted in countries such as China, the United States, Denmark, Australia, Iran, and Venezuela. The average age of participants varied widely, covering adolescents to middle-aged adults. Regarding the underlying psychiatric conditions, most patients had schizophrenia or schizoaffective disorder (as diagnosed by DSM-IV/DSM-V or ICD-10), with some studies including bipolar disorder, first-episode psychosis, or autism spectrum disorders. All participants received antipsychotic treatment, such as clozapine and olanzapine. All trials included a placebo-controlled group, with intervention durations ranging from 12 to 40 weeks. Overall, the included studies exhibited some heterogeneity in disease types, intervention drugs, dosage regimens, and geographic locations, but all used a randomized controlled design, providing a solid data foundation for this meta-analysis. (Table 1 ) Table 1 Characteristics of Included Trials. Study Age Country Primary diagnosis of psychiatric disorder Background antipsychotic Duration (weeks) Groups Experimental regimen (dose, frequency) Numbers (n) Intervention Control Intervention Control 1 Dan Siskind, 2025 38.9 Australia Schizophrenia or schizoaffective disorder (DSM-IV) Clozapine 36 Semaglutide Placebo Weekly subcutaneous semaglutide, titrated to 2.0 mg weekly 15 16 2 Ashok A. Ganeshalingam, 2025 38.3 Denmark Schizophrenia, schizotypal disorder, or schizoaffective disorder (ICD-10) Various antipsychotics (unspecified) 30 Semaglutide Placebo Weekly subcutaneous semaglutide, titrated to 1.0 mg weekly 77 76 3 Marie R. Sass, 2024 35 Denmark Schizophrenia spectrum disorder (ICD-10 or DSM-V) Clozapine or olanzapine 26 Semaglutide Placebo Weekly subcutaneous semaglutide, titrated to 1.0 mg weekly 36 37 4 Susan L. McElroy, 2024 42.6 USA Bipolar disorder type I or II (DSM-IV) Various antipsychotics (unspecified) 40 Liraglutide Placebo Daily subcutaneous liraglutide, titrated to 3.0 mg daily 29 31 5 Tiannan Shao, 2023 22.8 China Schizophrenia (DSM-V) Various antipsychotics (unspecified) 24 Metformin Placebo Daily oral metformin, titrated to 1500 mg daily 40 23 6 Charmaine Tang, 2022 24.5 Singapore First-episode psychosis including schizophrenia, schizoaffective disorder, etc. (DSM-IV) Various antipsychotics (unspecified) 24 Metformin Placebo Daily oral metformin, titrated to 1500 mg daily 5 8 7 Sri Mahavir Agarwal, 2021 31.6 Canada Schizophrenia, schizoaffective disorder, or bipolar disorder (DSM-V) Various antipsychotics (unspecified) 16 Metformin Placebo Daily oral metformin, titrated to 1500 mg daily 14 8 8 Clare A. Whicher, 2021 44 UK Schizophrenia, schizoaffective disorder, or first-episode psychosis Various antipsychotics (unspecified) 24 Liraglutide Placebo Daily subcutaneous liraglutide, titrated to 3.0 mg daily 15 19 9 Christoph U. Correll, 2020 13.7 USA Schizophrenia spectrum disorder, bipolar spectrum disorder, or psychotic depression (DSM-IV) Various antipsychotics (unspecified) 24 Metformin Placebo Daily oral metformin, titrated to 500–1000 mg twice daily 47 44 10 Julie R Larsen, 2017 42.5 Denmark Schizophrenia spectrum disorde (ICD-10 or DSM-IV) Clozapine or olanzapine 16 Liraglutide Placebo Daily subcutaneous liraglutide, titrated to 1.2–1.8 mg daily 47 50 11 P. L. Ishøy, 2017 35.8 Denmark Schizophrenia or schizoaffective disorder (ICD-10) Various antipsychotics (unspecified) 16 Exenatide Placebo Weekly subcutaneous exenatide, 2 mg weekly 20 20 12 Dan J Siskind, 2018 / Australia Schizophrenia or schizoaffective disorder Clozapine 24 Exenatide Placebo Weekly subcutaneous exenatide, 2 mg weekly 14 14 13 Pelle L Ishøy, 2017 35.9 Denmark Schizophrenia or schizoaffective disorder (ICD-10) Various antipsychotics (unspecified) 12 Exenatide Placebo Weekly subcutaneous exenatide, 2 mg weekly 20 20 14 Evdokia Anagnostou, 2016 12.8 USA Autism Spectrum Disorder (DSM-IV) Various antipsychotics (unspecified) 16 Metformin Placebo Daily Oral metformin liquid, titrated to 500–850 mg twice daily. 28 32 15 Jeffrey Rado, 2016 36.4 USA Schizophrenia, schizoaffective disorder, bipolar disorder, major depression with psychotic features (DSM-IV) Olanzapine 24 Metformin Placebo Daily Oral metformin, titrated to 2000 mg daily 12 13 16 R-R Wu, 2016 26 China First-episode schizophrenia (DSM-IV) Various antipsychotics (unspecified) 24 Metformin Placebo Daily oral metformin, titrated to 500 mg twice daily 99 91 17 Chih-Chiang Chiu, 2016 47.3 China Schizophrenia or schizoaffective disorder (DSM-IV) Clozapine 12 Metformin Placebo Daily oral metformin, titrated to 500 mg twice daily 19 18 18 Paria Hebrani, 2015 46.5 Iran Schizophrenia (DSM-IV-TR) Clozapine 20 Metformin Placebo Oral metformin 500 mg twice daily 19 18 19 Chun-Hsin Chen, 2013 41.6 China Schizophrenia or schizoaffective disorder (DSM-IV) Clozapine 24 Metformin Placebo Oral metformin 500 mg three times daily 28 27 20 L. Fredrik Jarskog, 2013 43.2 USA Schizophrenia or schizoaffective disorder (DSM-IV) Various antipsychotics (unspecified) 16 Metformin Placebo Daily oral metformin, titrated to 1000 mg twice daily 75 71 21 Man Wang, 2012 26.2 China First-episode schizophrenia (DSM-IV) Various antipsychotics (unspecified) 12 Metformin Placebo Daily oral metformin, titrated to 500 mg twice daily 32 34 22 Ren-Rong Wu, 2012 26.4 China First-episode schizophrenia (DSM-IV) Various antipsychotics (unspecified) 24 Metformin Placebo Daily oral metformin, titrated to 1000 mg daily 39 37 23 Edgardo Carrizo, 2009 38.9 Venezuela Schizophrenia, Bipolar I Disorder and Schizophreniform Disorder (DSM-IV) Clozapine 14 Metformin Placebo Daily oral metformin, titrated to 1000 mg daily 24 30 24 Ren-Rong Wu, 2008 26.3 China First-episode schizophrenia (DSM-IV) Various antipsychotics (unspecified) 12 Metformin Placebo Oral metformin 250 mg three times daily 32 32 25 Ren-Rong Wu, 2008 25.1 China First-episode schizophrenia (DSM-IV) Olanzapine 12 Metformin Placebo Oral metformin 250 mg three times daily 18 19 26 Trino Baptista, 2007 42.8 Venezuela First-episode schizophrenia (DSM-IV) Various antipsychotics (unspecified) 12 Metformin Placebo Daily oral metformin 850–2550 mg 36 36 27 Trino Baptista, 2007 47.7 Venezuela Schizophrenia (DSM-IV) Fluphenazine decanoate, levomepromazine 16 Metformin Placebo Daily oral metformin 850–2550 mg 15 15 28 David J. Klein, 2006 13.4 USA Bipolar disorder, attentional disorders, schizophrenia Various antipsychotics (unspecified) 16 Metformin Placebo Daily oral metformin, titrated to 850 mg twice daily 15 15 29 Trino Baptista, 2006 47.7 Venezuela Severe schizophrenia or schizoaffective disorders Olanzapine 14 Metformin Placebo Daily oral metformin 850–1700 mg daily 19 18 3.2 ROB2 Literature Quality Assessment Using the ROB2.0 tool for quality assessment, 17 of the 29 included studies were rated as low risk, while 12 were classified as having some concerns. ( Figure S1 ) 3.3 Results of network meta-analysis with different outcomes For BMI as the outcome, a total of 27 studies involving 1,625 participants were included. (Fig. 2 A) Compared to placebo, Semaglutide (MD = -3.55, 95% CI: -4.27 to -2.84), Liraglutide (MD = -1.56, 95% CI: -2.33 to -0.79), and Metformin (MD = -1.12, 95% CI: -1.44 to -0.80) all significantly reduced BMI. Semaglutide also demonstrated a significant advantage over Liraglutide (MD = -1.99, 95% CI: -3.05 to -0.94), Exenatide (MD = -2.37, 95% CI: -3.88 to -0.85), and Metformin (MD = -2.43, 95% CI: -3.22 to -1.65) in terms of BMI reduction, highlighting its important advantages. The SUCRA values indicated that the efficacy ranking of the four medications in reducing BMI was Semaglutide (99.9%), Liraglutide (63%), Exenatide (45.8%), and Metformin (40.1%). (Fig. 3 A, 4 ) For WC as the outcome, a total of 20 studies involving 1,114 participants were included. (Fig. 2 A) Semaglutide (MD = -6.34, 95% CI: -8.17 to -4.51), Liraglutide (MD = -3.82, 95% CI: -5.42 to -2.23), and Metformin (MD = -1.12, 95% CI: -1.44 to -0.80) significantly reduced WC compared to placebo. Similarly, Semaglutide demonstrated a significant effect in reducing waist circumference compared to Liraglutide (MD = -2.51, 95% CI: -4.94 to -0.09), Exenatide (MD = -3.92, 95% CI: -7.57 to -0.27), and Metformin (MD = -5.05, 95% CI: -7.08 to -3.02), indicating its efficacy in weight loss. The SUCRA values indicated the efficacy ranking of the four medications in reducing WC as follows: Semaglutide (99%), Liraglutide (70.2%), Exenatide (47.4%), and Metformin (31.5%). (Fig. 3 A, 4 ) For HbA1c as the outcome, 15 studies comprising 931 participants were included. (Fig. 2 B) Semaglutide (MD = -0.44, 95% CI: -0.53 to -0.35), Exenatide (MD = -0.24, 95% CI: -0.39 to -0.09), Liraglutide (MD = -0.23, 95% CI: -0.32 to -0.15), and Metformin (MD = -0.08, 95% CI: -0.14 to -0.03) all significantly lowered HbA1c compared to placebo. Semaglutide also showed a significant effect on HbA1c reduction compared to Exenatide (MD = -0.20, 95% CI: -0.37 to -0.02), Liraglutide (MD = -0.20, 95% CI: -0.33 to -0.08), and Metformin (MD = -0.35, 95% CI: -0.46 to -0.25). Liraglutide (MD = -0.15, 95% CI: -0.25 to -0.05) significantly reduced HbA1c compared to Metformin, while Exenatide (MD = -0.16, 95% CI: -0.32 to 0.00) approached significance. These findings indicated clear efficacy of these medications for weight loss. The SUCRA values illustrated the ranking of the four medications in lowering HbA1c: Semaglutide (99.7%), Exenatide (62.8%), Liraglutide (61.7%), and Metformin (25.7%), all outperforming placebo. The outstanding efficacy of GLP-1 agonists in glycemic control and weight reduction warranted further attention. (Fig. 3 B, 4 ) For FBG as the outcome, 15 studies with 1,534 participants were included. (Fig. 2 B) Semaglutide (MD = -0.53, 95% CI: -0.88 to -0.18) and Metformin (MD = -0.21, 95% CI: -0.37 to -0.06) significantly lowered FBG compared to placebo. The SUCRA values indicated the efficacy ranking of the four medications in reducing FBG as follows: Semaglutide (84.5%), Exenatide (68.4%), Liraglutide (47%), and Metformin (43.9%), all superior to placebo. (Fig. 3 B, 4 ) For TC as the outcome, 16 studies involving 1,153 participants were included. (Fig. 2 C) Only Metformin (MD = -0.31, 95% CI: -0.59 to -0.03) demonstrated a significant reduction compared to placebo, while other medications showed a reducing trend but did not achieve statistical significance. The SUCRA rankings for these medications compared to placebo were as follows: Metformin (78.1%), Liraglutide (69.1%), Exenatide (48.8%), Placebo (29.1%), and Semaglutide (24.9%). (Fig. 3 C, 4 ) For TG as the outcome, 20 studies including 1,261 participants were analyzed. (Fig. 2 C) Only Metformin (MD = -0.16, 95% CI: -0.31 to -0.01) showed a significant reduction compared to placebo, while Exenatide and Liraglutide demonstrated a reducing trend without achieving statistical significance. The SUCRA rankings for these medications compared to placebo were as follows: Metformin (79.9%), Liraglutide (63.7%), Semaglutide (38.7%), Placebo (34%), and Exenatide (33.8%). (Fig. 3 C, 4 ) For the outcome of HDL-C, a total of 19 studies involving 1,236 participants were included. (Fig. 2 D) All four medications showed an increasing trend in HDL-C levels compared to the placebo, though none reached statistical significance (Exenatide: 0.08; Liraglutide: 0.08; Metformin: 0.02; Semaglutide: 0.02). The SUCRA rankings for the four medications were as follows: Exenatide (74.9%), Liraglutide (72.6%), Metformin (42.6%), and Semaglutide (40.9%), all demonstrating superiority over the placebo. (Fig. 3 D, 4 ) For the outcome of LDL-C, 14 studies involving 1,031 participants were included. (Fig. 2 D) Three medications exhibited a decreasing trend in LDL-C levels compared to the placebo but did not achieve statistical significance (Liraglutide: -0.11; Metformin: -0.25; Semaglutide: -0.06). The SUCRA values ranked as follows: Metformin (76.5%), Liraglutide (53.4%), Semaglutide (45.8%), and Exenatide (42%), all surpassing the placebo. (Fig. 3 D, 4 ) For the outcome of blood pressure, 13 studies involving 697 participants were considered. (Fig. 2 E) None of the different medications achieved statistical significance in analyses for reducing SBP and DBP. The SUCRA rankings for SBP were as follows: Exenatide (87.5%), Metformin (67.6%), Semaglutide (58.3%), and Liraglutide (19.5%), all outperforming the placebo. Similarly, the SUCRA rankings for DBP were Exenatide (83.6%), placebo (50.9%), Semaglutide (45.2%), Metformin (41.3%), and Liraglutide (29.1%), all showing better results than the placebo. (Fig. 3 E, 4 ) Regarding psychiatric scale scores, 12 studies encompassing 675 participants were analyzed. (Fig. 2 F) Compared to the placebo, Metformin demonstrated a significant effect in lowering mental scale scores (SMD = -0.35, 95% CI: -0.61 to -0.09). The SUCRA rankings from highest to lowest were as follows: Metformin (93.9%), placebo (42.4%), Exenatide (42.3%), Liraglutide (38.8%), and Semaglutide (32.7%). (Fig. 3 F, 4 ) 3.4 Sensitivity Analysis For all outcome measures, a sensitivity analysis was performed using the leave-one-out method to assess the influence of individual studies on the network effect estimates. In each round of analysis, one study was excluded, and the remaining studies were re-analyzed using a random-effects consistency network meta-analysis. The results showed that for HbA1c, after excluding certain studies, the significance of semaglutide, metformin, and placebo comparisons disappeared, but the direction of the combined effect remained consistent. For TC, TG, and psychiatric rating scores, the direction of effect remained consistent, although significance was lost after excluding a few studies. This might be related to the limited research available on some GLP-1RAs. For the majority of other sensitivity analyses, the direction of the combined effect remained consistent, the change in effect size was minimal, confidence intervals were highly overlapping, and there were no substantial changes in statistical significance, indicating that the results were robust. ( Table S2 ) 3.5 Meta-Regression Analysis For the 11 outcome measures mentioned above, univariate network meta-regression analyses were performed with five covariates, to explore whether these factors influenced the relative effects of different drug interventions. The regression analysis results showed that, for the outcome of HbA1c change, a statistically significant regression coefficient was found when comparing exenatide with liraglutide, with the mean age as a covariate. This suggests that, in this comparison, the baseline age of the patients may have moderated the treatment effect. Additionally, for all other outcome measures and the five covariates, no statistically significant associations were found. This result supports the robustness of the primary conclusions of the study across different clinical contexts. It is worth noting that due to the limited number of studies involving exenatide and liraglutide, we were unable to conduct further subgroup analyses to better explore this moderating effect. ( Table S3 ) 3.6 Bias Assessment and Evidence Grading Funnel plots were constructed for all outcome measures to assess publication bias( 28 ). ( Fig S3 ) The distribution of study points was approximately symmetric, with no noticeable outliers, suggesting a low likelihood of publication bias in this study. Using the CINeMA framework, the evidence quality for all outcome measures was assessed. The results revealed differences in the evidence grading across the outcome measures. (Fig. 3 ) 4 Discussion The management of AIMD remains a core challenge in psychiatric clinical practice, as it requires balancing rehabilitation with safety( 7 ). Recently, the widespread use and favorable safety profile of metformin have been recognized as foundational in addressing this issue. The World Health Organization (WHO) published the "Management of physical health conditions in adults with severe mental disorders" in 2018, which suggests the adjunctive use of metformin when interventions and/or changes in antipsychotic medications are ineffective( 60 ). More recently, GLP-1 RAs have demonstrated remarkable efficacy in treating metabolic disorders, progressively becoming a valuable adjunct in managing AIMD effects, showing distinct advantages. Despite several recent systematic reviews and network meta-analyses evaluating the value of interventions for AIMD, including various medications, some of these have been withdrawn from clinical use due to safety concerns( 8 , 10 ). Additionally, these analyses have primarily focused on weight changes as the main outcome, failing to comprehensively assess the impact of multidimensional parameters such as blood lipids, blood pressure and psychiatric symptoms. With the growing evidence supporting the use of GLP-1 RAs in metabolic health, recent high-quality RCTs have been conducted since 2025, specifically exploring their effects in populations with psychiatric disorders. This study, through network meta-analysis, for the first time systematically compared the effects of metformin with different GLP-1 RAs (semaglutide, liraglutide, exenatide) on multiple outcome indicators and psychiatric symptoms in patients under antipsychotic treatment. By constructing a more clinically relevant and comprehensive psychiatric evaluation framework, this research provided direct and higher-level evidence for the development of individualized treatment strategies that balance both health and stability. First-generation antipsychotics (FGAs) exert their therapeutic effects primarily through antagonism of dopamine D2 receptors. In contrast, second-generation antipsychotics (SGAs), which have emerged over the past three decades, generally combine D2 receptor antagonism or partial agonism with 5-HT 2 A receptor antagonism and, in some cases, 5-HT 1 A receptor agonism( 61 ). The disturbances in glucose and lipid metabolism induced by these agents represent a complex pathological process involving bidirectional interactions between the central nervous system and multiple peripheral organs. The central mechanisms underlying AIMD are thought to originate from cascade reactions triggered by blockade of a broad network of brain receptors, ultimately producing widespread perturbations of the energy homeostasis regulatory system. As the principal integrative center for energy balance, the hypothalamus is considered a primary target of antipsychotic action. The drug disrupts the balance of feeding-regulating neurons by antagonizing the 5-HT 2 C and histamine H1 receptors in key hypothalamic nuclei such as the arcuate nucleus (ARC) and the periventricular nucleus (PVH). Consequently, the activity of anorexigenic pro-opiomelanocortin (POMC) neurons is suppressed( 62 ), whereas orexigenic agouti-related peptide/neuropeptide Y (AgRP/NPY) neurons are activated, shifting hypothalamic neuropeptide output toward a hunger-promoting profile( 63 ). In addition, blockade of histamine H1 receptors has been associated with aberrant activation of AMP-activated protein kinase (AMPK) signaling( 64 , 65 ) in the ARC and mediobasal hypothalamus (MBH). The activation of AMPK, which serves as a cellular "energy sensor," is believed to positively regulate appetite( 66 ). Antipsychotic effects are not restricted to the hypothalamus but extend to the mesolimbic dopaminergic reward circuitry( 67 ). By modulating projections from the ventral tegmental area (VTA) to the nucleus accumbens (NAc), food reward valuation and food-seeking motivation are altered( 68 ). Concurrently, integration of gastrointestinal satiety signals by the nucleus tractus solitarius (NTS) in the brainstem may be attenuated( 69 ). Collectively, these changes promote hyperphagia, reduce satiety, and increase hedonic feeding, thereby constituting a central origin and key driver of antipsychotic-associated dysregulation of glucose and lipid metabolism. Beyond central dysregulation of appetite control, peripheral effects induced by antipsychotics are widespread and direct. In the pancreas, α- and β-cells are capable of synthesizing dopamine (DA) and maintaining glycemic homeostasis through autocrine/paracrine signaling. By antagonizing dopamine receptors expressed on these cells, antipsychotics are proposed to interrupt this critical local modulatory pathway, thereby directly disrupting islet hormone secretion and glycemic control( 70 ). In the liver, antipsychotics have been reported to promote the expression of key gluconeogenic genes via activation of the PI3K–AKT–FOXO1 signaling axis( 71 ), contributing to fasting hyperglycemia. In parallel, hepatic metabolic programs may be coordinately reconfigured through upregulation of TCF7L2, Chrm3, ALK, and the regulatory factor Ptprz1( 72 ), thereby enhancing de novo lipogenesis and exacerbating dyslipidemia( 73 ). In adipose tissue, antipsychotic-induced leptin resistance has been implicated as an important contributor to fat accumulation and metabolic impairment( 74 ). Moreover, abnormal fluctuations in adiponectin—an insulin-sensitizing adipokine—may further disrupt metabolic crosstalk between adipose tissue and central/peripheral organs. A substantial body of evidence also indicates that antipsychotics can stimulate SREBP-mediated lipogenesis through both direct and indirect mechanisms( 75 , 76 ). In skeletal muscle, insulin resistance is aggravated through interference with the PI3K/Akt–GLUT4 insulin signaling axis( 77 ), accumulation of intracellular lipotoxic metabolites, and impairment of mitochondrial energy metabolism( 78 ). In the setting of this multi-pathway dysregulation, metformin has demonstrated distinctive value as a foundational antihyperglycemic agent with multi-organ, integrated regulatory effects. In the present study, metformin was shown to significantly improve multiple metabolic endpoints compared with placebo. Notably, with respect to lipid-related outcomes, the available evidence suggests that metformin may be the only intervention achieving statistically significant improvements in TC and TG. These benefits appear to be mediated primarily through modulation of AMPK, a core cellular energy sensor. In peripheral tissues such as the liver and skeletal muscle, AMPK activation by metformin is associated with reductions in blood glucose( 79 ). Downstream cascades include inhibition of acetyl-CoA carboxylase (ACC) with enhanced fatty-acid oxidation( 80 ), as well as suppression of key gluconeogenic enzyme expression, thereby reducing hepatic glucose output( 81 ). Given that antipsychotics can promote SREBP-dependent lipogenesis, metformin has been proposed to counteract antipsychotic-induced lipid droplet accumulation by activating AMPK and suppressing SREBP1 and its downstream targets( 82 ). This mechanism is consistent with our meta-analytic findings indicating a relative advantage of metformin in improving lipid metabolism. Beyond its canonical effects on insulin signaling and fatty-acid oxidation, metformin has also been reported to markedly regulate hepatic gene programs governing lipid synthesis and clearance. Experimental evidence indicates that, in hepatocytes, PCSK9 expression can be suppressed and SREBP2 downregulated by metformin, leading to increased LDL receptor (LDLR) abundance and enhanced LDL-cholesterol (LDL-C) clearance( 83 ). In animal and cellular models of antipsychotic-induced hepatic lipid deposition, expression of lipogenic genes (e.g., PCSK9, FAS, and SCD1) has been reduced by metformin, whereas PPARα and fatty-acid β-oxidation–related genes have been restored, thereby markedly reversing intrahepatic lipid accumulation( 84 ). Interestingly, hypothalamic actions of metformin appear to be mechanistically distinct. Metformin has been reported to cross the blood–brain barrier and to limit excessive increases in appetite by decreasing NPY and AgRP mRNA expression and activating STAT3 signaling. Notably, this process has been described as occurring without hypothalamic AMPK activation( 85 ). Earlier studies further suggested that AMPK activity in hypothalamic neurons can be inhibited by metformin, thereby modulating expression of the orexigenic peptide NPY( 86 ). Collectively, through multidimensional mechanisms, metformin may serve as a cornerstone therapy capable of comprehensive and early intervention for antipsychotic-associated dysregulation of glucose and lipid metabolism. This study also indicated that GLP-1 RAs provide uniquely robust efficacy for glycemic control and weight reduction in patients with AIMD, with greater improvements than metformin in BMI, WC, HbA1c, and FBG. GLP-1 is an incretin hormone secreted primarily by intestinal L cells, and GLP-1 RAs are glucose-lowering agents that mimic its physiological actions. In a mouse model of long-term olanzapine exposure, GLP-1 receptor expression in β cells was reported to be markedly reduced, with concomitant worsening of glucose dysregulation. Moreover, mice with conditional deletion of the Wnt-pathway transcription factor Tcf7l2 were more susceptible to olanzapine-induced metabolic abnormalities. Together, these findings suggest that regulation of the GLP-1 axis may influence vulnerability to antipsychotic-associated metabolic injury. Within the central nervous system, GLP-1 RAs are thought to directly activate hypothalamic satiety circuits and enhance pro-opiomelanocortin (POMC) neuronal activity, producing a strong and sustained sensation of satiety( 87 , 88 ). More recent work has suggested complex interactions between GLP-1 receptor–expressing neurons in the dorsomedial hypothalamus (DMH) and ARC neuropeptide Y (NPY)/agouti-related peptide (AgRP) neurons, jointly shaping the regulation of food intake( 89 ). In addition to suppressing hunger at the hypothalamic level, GLP-1 RAs have also been reported to act on mesolimbic reward circuits (e.g., VTA/NAc), directly modulating dopaminergic neurotransmission and attenuating food reward( 90 ). This effect conceptually align with mechanisms implicated in antipsychotic-induced hyperphagia. Beyond potent central appetite suppression, weight loss and metabolic improvement are further supported by multiple peripheral mechanisms, including delayed gastric emptying, enhanced glucose-dependent insulin secretion, suppression of glucagon release, reduced adipose storage, and attenuation of inflammatory responses. With the rapid expansion of GLP-1 RA use in recent years, these glucose-lowering and weight-reducing mechanisms have been extensively investigated( 91 ). Owing to their strong effects on weight reduction and glycemic control, GLP-1 RAs may offer a more efficient therapeutic option for patients with AIMD and could be prioritized in selected individuals. However, the mechanisms and clinical effects of GLP-1 RAs on mental health outcome remain controversial. Some clinical studies and systematic reviews have suggested potential antidepressant benefits( 92 ), whereas multiple observational and cohort studies have raised concerns. In several large real-world datasets, GLP-1 RA use has been reported to be positively associated with the risk of psychiatric disorders, including major depression and anxiety( 93 ). In addition, case reports and pharmacovigilance-database analyses have linked GLP-1 RAs to mood worsening, suicidal ideation, or other psychiatric symptoms in a subset of patients( 94 , 95 ). Consequently, concern persists in clinical practice regarding the potential for psychiatric symptom exacerbation, particularly among individuals with a prior psychiatric history or elevated vulnerability to mood disorders. These uncertainties highlight the need for larger, rigorously designed randomized controlled trials and mechanistic studies to clarify the true impact of GLP-1 RAs on mental health and to provide a more reliable evidence base for clinical decision-making. In the present study, a favorable trend was observed for metformin in improving schizophrenia-related rating scale scores, suggesting potential psychiatric benefits beyond metabolic regulation alone. Such multidimensional symptom improvement may be attributable to complex, synergistic, and multi-target actions of metformin within the central nervous system. First, brain energy homeostasis may be directly improved. Patients with schizophrenia frequently exhibit abnormal cerebral glucose metabolism and mitochondrial dysfunction, particularly in the prefrontal cortex and hippocampus( 96 ). Metformin has been reported to cross the blood–brain barrier and to activate AMPK, thereby systemically modulating energy metabolism( 97 ). Evidence indicates that metformin can normalize peripheral abnormalities in tricarboxylic acid (TCA) cycle–related metabolites in patients with schizophrenia( 98 ), including reductions in lactate and increases in citrate and succinate levels. These changes suggest improved mitochondrial oxidative metabolism efficiency and a greater supply of energy substrates required for synaptic plasticity and high-demand neuronal activity. Notably, such metabolic remodeling has been significantly associated with improvements in working memory and verbal learning. Second, neurohealth may be indirectly promoted through anti-inflammatory and antioxidative mechanisms. Chronic low-grade inflammation and oxidative stress are key pathophysiological components of major depressive disorder( 99 ) and schizophrenia, and can damage neurons and impede neuroplasticity. Excessive microglial activation has been shown to be suppressed by metformin, accompanied by reduced release of proinflammatory cytokines and enhancement of endogenous antioxidant defenses( 100 ). In a methamphetamine-induced neurotoxicity model, metformin exhibited clear neuroprotective effects, alleviating oxidative stress and hippocampal inflammation. These effects were closely linked to modulation of the Akt/GSK3β signaling pathway( 101 ). More importantly, neuroplasticity and functional connectivity may be enhanced by metformin. Impaired neuroplasticity—particularly downregulation of brain-derived neurotrophic factor (BDNF) signaling involved in learning and memory—has been recognized as a core mechanism underlying cognitive deficits in schizophrenia. Through activation of the AMPK/CREB pathway, BDNF expression in the hippocampus and other regions has been reported to be upregulated by metformin. Functional magnetic resonance imaging studies further showed that, following adjunctive metformin treatment, functional connectivity was strengthened between the right caudate/hippocampus and the ventral occipital cortex as well as the middle frontal gyrus( 34 ). Because these regions are implicated in memory formation, visual information processing, and working memory, enhanced connectivity provides a systems-level circuit explanation for the observed improvements in cognitive performance scores. On the basis of our findings and the existing evidence, management of AIMD should be guided by an integrated, proactive, and individualized strategy. The starting point should be earlier monitoring and continuous, longitudinal oversight. To achieve this shift from reactive treatment to proactive prevention( 102 ), collaborative care models should be established between psychiatrists and endocrinologists or primary-care clinicians, with metabolic parameters incorporated into routine psychiatric follow-up. With respect to pharmacotherapy, more precise selection and targeted intervention should be implemented. When a single metabolic abnormality predominates, agent selection may be guided by the comparative findings of this study. However, given that multiple metabolic disturbances frequently coexist, combination therapy with metformin plus a GLP-1 RA may represent a preferable option. Further studies are needed to validate potential synergistic effects and to establish long-term safety. Importantly, all management decisions should be anchored in the core principle that metabolic and psychiatric health are equally prioritized( 103 ). The ultimate goal is not merely normalization of metabolic indices, but improvement of overall physical health to strengthen treatment confidence and adherence, thereby providing a more stable physiological foundation for psychiatric recovery and ultimately achieving a dual benefit for both physical and mental health. Several limitations should be acknowledged, despite the use of a network meta-analysis to compare pharmacologic strategies for AIMD. First, the number of included studies and total sample size remained limited, particularly for drug-specific analyses of individual GLP-1 RAs, which constrained more granular subgroup assessments. Second, because individual participant data were unavailable, baseline confounders could not be further adjusted for, and potential effect modifiers could not be rigorously examined. Third, direct head-to-head comparisons between GLP-1 RAs and metformin were lacking in the current evidence network. Consequently, treatment ranking relied primarily on indirect comparisons. Although the consistency assessment supported the inference, confirmation in future direct comparative trials is required. Fourth, sensitivity analyses indicated that statistical significance for certain outcomes changed after exclusion of individual studies, suggesting that some results may have been influenced by single-study effects. Although the overall direction of effect was preserved, cautious interpretation is warranted. Finally, several included trials had small sample sizes (e.g., Study 6), which may have introduced small-study bias. Future research should prioritize larger, multicenter randomized controlled trials with longer follow-up, particularly those conducting direct comparisons among active agents and collecting individual-level data, to further validate these conclusions and to provide higher-level evidence for precision and personalized clinical management. 5 Conclusion This network meta-analysis systematically compared the multidimensional efficacy of metformin and three GLP-1 RAs in improving AIMD. The results indicated that different medications exhibit distinct advantages in improving metabolic parameters, suggesting that clinical choices should be individualized based on specific metabolic abnormalities. This study provides comprehensive evidence for clinicians in managing antipsychotic-induced metabolic risks, emphasizing the importance of considering the potential impact on psychiatric symptoms while striving for improvements in metabolic outcomes. It advocates for the development of a patient-centered, integrated management strategy that balances both metabolic and mental health. Financial disclosure statement : This study was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2024-JYB-JBZD-011, 2023-JYB-JBZD-003), and the High-Level Hospital Project of Dongzhimen Hospital, Beijing University of Chinese Medicine (Grant Nos. DZMG-LJRC0001), National Administration of Traditional Chinese Medicine High-Level Key Discipline: Traditional Chinese Medicine Endocrinology (404053503). Declarations Conflict -of-interest : The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Author Contribution Ye-xin Chen, Qian-wen Yang, Mao-xuan Lin: Co-first authors. 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03:09:01","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9317100/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9317100/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107870165,"identity":"b729811f-bffc-48db-a77e-1822bcad40cc","added_by":"auto","created_at":"2026-04-27 07:38:59","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128232,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart according to the Preferred Reporting Items for Systematic Reviews and Meta analyses (PRISMA) guideline.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/a8a3c39f537cc9d648cd0659.jpg"},{"id":107870657,"identity":"922b75f7-9088-492e-85dd-2daaaea7e19a","added_by":"auto","created_at":"2026-04-27 07:40:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":295854,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork of eligible comparisons. A. Body Mass Index (BMI); B. Waist Circumference (WC); C. Glycated Hemoglobin A1c (HbA1c); D. Fasting Blood Glucose (FBG); E. Total Cholesterol (TC); F. Triglyceride (TG); G. High-Density Lipoprotein (HDL-C); H. Low-Density Lipoprotein (LDL-C); I. Systolic Blood Pressure (SBP); J. Diastolic Blood Pressure (DBP); K.Psychiatric Rating Scale.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/203b86b0e823a0cf7d74f5f9.png"},{"id":107869917,"identity":"9da1abe6-2924-487c-a762-b7548f920648","added_by":"auto","created_at":"2026-04-27 07:38:27","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":90912,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork meta-analysis results for different outcomes.\u003c/p\u003e\n\u003cp\u003eA. Body Mass Index (BMI); B. Waist Circumference (WC); C. Glycated Hemoglobin A1c (HbA1c); D. Fasting Blood Glucose (FBG); E. Total Cholesterol (TC); F. Triglyceride (TG); G. High-Density Lipoprotein (HDL-C); H. Low-Density Lipoprotein (LDL-C); I. Systolic Blood Pressure (SBP); J. Diastolic Blood Pressure (DBP); K. Psychiatric Rating Scale.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/3f9de2504eb22a4af5811d44.jpg"},{"id":107832924,"identity":"0d9f024e-026c-4542-b3ee-af7efd8dbb56","added_by":"auto","created_at":"2026-04-26 15:38:03","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":87223,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of SUCRA for different outcomes.\u003c/p\u003e\n\u003cp\u003eBMI, Body Mass Index; WC, Waist Circumference; HbA1c, Glycated Hemoglobin A1c; FBG, Fasting Blood Glucose; TC, Total Cholesterol; TG, Triglyceride; HDL-C, High-Density Lipoprotein; LDL-C, Low-Density Lipoprotein; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/38143cda56868eb21256318e.jpg"},{"id":107872358,"identity":"d5a4f819-8426-4455-b7c1-2ccc6acd6aa3","added_by":"auto","created_at":"2026-04-27 07:56:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1108073,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/d27c6936-1078-4edf-acc2-6e1d0cb54d26.pdf"},{"id":107832920,"identity":"bb33ce23-5aba-4a78-95e3-5c81b36f6fef","added_by":"auto","created_at":"2026-04-26 15:38:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2651219,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineSupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9317100/v1/f35f6b022e0bba6da8c631c7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Comparative Effectiveness of Metformin versus GLP-1 Receptor Agonists in Treating Antipsychotic-Induced Metabolic Disturbances: A Systematic Review and Network Meta-Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eMental disorders, particularly severe psychiatric conditions such as schizophrenia, impose asignificant global burden on public health. These conditions not only profoundly affect patients' cognition, emotions, and daily functioning, but also lead to a marked reduction in overall life expectancy(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e).A study has shown that individuals with schizophrenia face a higher risk of mortality compared to the general population, with cardiovascular diseases and metabolic complications being major non-psychiatric causes of death. The overall life expectancy of these patients can be shortened by 10 to 20 years, a disparity largely attributed to the accumulation of metabolic risk factors and increased cardiovascular burden(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAs societal awareness of mental health issues continues to grow, the use of antipsychotic medications has become more widespread and is now a first-line treatment for severe psychiatric disorders such as schizophrenia and bipolar disorder(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In clinical practice, particularly with second-generation antipsychotic drugs (SGAs), while these medications effectively improve psychiatric symptoms and reduce relapse rates, they are also associated with significant metabolic side effects. These adverse effects include weight gain, lipid metabolism abnormalities, insulin resistance, impaired glucose tolerance, and even the development of type 2 diabetes and hypertension(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), collectively referred to as Antipsychotic-Induced Metabolic Disturbances (AIMD). A meta-analysis involving data from 35,007 participants demonstrated a significant association between the long-term use of medications such as chlorpromazine, clozapine, and olanzapine, and various metabolic disorders, including weight gain and deterioration in blood glucose and lipid parameters(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). An observational cohort study of 767 patients with schizophrenia found that clozapine and olanzapine significantly increased BMI and led to several adverse metabolic changes. Furthermore, metabolic risks varied across different SGAs, suggesting the need for clinical monitoring of these metabolic side effects during long-term treatment.\u003c/p\u003e \u003cp\u003eVarious intervention strategies have been implemented in clinical practice to address AIMD. Early studies and clinical guidelines have recommended metformin as a first-line drug to prevent or alleviate these metabolic side effects(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Several randomized controlled trials (RCT) and comprehensive meta-analyses have shown that metformin, as an adjunctive treatment, significantly reduces weight gain and lowers BMI, with additional improvements in metabolic parameters such as blood lipids(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). With the widespread use and successful application of GLP-1 receptor agonists (GLP-1RAs) in the treatment of diabetes and obesity, their potential efficacy in addressing AIMD has gradually garnered attention(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Existing systematic reviews and clinical trials suggest that GLP-1 RAs can improve weight, blood glucose, and other metabolic parameters in patients with AIMD, with good tolerability(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the current studies are mostly small-sample, short-term follow-up trials, and there is a lack of high-quality meta-analyses that provide a comprehensive summary of broader outcome measures, such as lipid profiles, blood pressure, and psychiatric symptom(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn conclusion, although research has explored the therapeutic effects of metformin and GLP-1 RAs on AIMD, comprehensive evidence regarding broader metabolic indicators and psychiatric symptoms is still urgently needed. Furthermore, there is a lack of high-quality network meta-analyses that directly compare the two most common AIMD medications, metformin and GLP-1 RAs, regarding their effects on various metabolic outcomes. Therefore, this study aims to conduct a network meta-analysis to systematically integrate the multidimensional efficacy of metformin and GLP-1 RAs in the treatment of AIMD, providing more comprehensive evidence for clinical practice.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Materials and Methods\u003c/h2\u003e\n \u003cp\u003eThis network meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension statement for network meta-analyses. Due to the lack of direct comparisons between metformin and various GLP-1 RAs in AIMD patients, indirect comparisons were employed to predict the probability rankings of different treatment regimens regarding their metabolic efficacy and safety. To ensure transparency, reliability, and novelty, the study protocol was registered in the Prospective Register of Systematic Reviews(12, 13) (CRD420251159491).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.2 Data Sources and Search Strategy\u003c/h2\u003e\n \u003cp\u003eA systematic search was conducted in the PubMed, Embase, Cochrane Library, and Web of Science databases to identify randomized controlled trials evaluating the effects of metformin and GLP-1 RAs in the treatment of AIMD from the inception of each database up to December 1, 2025. The search strategy combined free-text and subject terms, with a restriction to English-language articles. Five reviewers (Chen YX, Yang QW, Sun MX, Dong YY, Zhang L) independently screened the titles and abstracts in duplicate. Discrepancies were resolved by consulting a sixth reviewer (Lin MX). Additionally, the reference lists of the included articles and relevant systematic reviews were screened to identify potentially eligible studies. A full list of the search strategy is available in \u003cstrong\u003eTable S1\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.3 Selection Criteria\u003c/h2\u003e\n \u003cp\u003eRandomized clinical trials with the following inclusion criteria were included: (1) patients receiving metformin or GLP-1 RA interventions; (2) patients undergoing at least one antipsychotic medication regimen; (3) a minimum follow-up duration of 12 weeks. Studies had to report at least one of the following outcome measures: (1) BMI, defined as weight (kg) divided by height (m) squared, consistently reported in units of kg/m\u0026sup2;; (2) waist circumference (WC), measured at the midpoint between the lower edge of the rib cage and the upper edge of the iliac crest using a soft tape measure, consistently reported in centimeters (cm); (3) fasting blood glucose (FBG), defined as venous plasma glucose concentration measured after at least 8 hours of fasting, consistently reported in mmol/L (with conversion of mg/dL to mmol/L using the factor 1 mmol/L\u0026thinsp;=\u0026thinsp;18 mg/dL); (4) Glycated Hemoglobin A1c (HbA1c), representing the average blood glucose level over the past 2\u0026ndash;3 months, consistently reported in percentage (%); (5) systolic blood pressure (SBP), defined as the arterial pressure during the contraction of the heart, consistently reported in mmHg; (6) diastolic blood pressure (DBP), defined as the arterial pressure during the relaxation phase of the heart, consistently reported in mmHg; (7) total cholesterol (TC), defined as the total cholesterol concentration from all lipoproteins in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L\u0026thinsp;=\u0026thinsp;38.67 mg/dL); (8) triglycerides (TG), defined as the concentration of triglycerides in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L\u0026thinsp;=\u0026thinsp;88.57 mg/dL); (9) high-density lipoprotein cholesterol (HDL-C), defined as the concentration of high-density lipoprotein cholesterol in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L\u0026thinsp;=\u0026thinsp;38.67 mg/dL); (10) low-density lipoprotein cholesterol (LDL-C) defined as the concentration of low-density lipoprotein cholesterol in serum, consistently reported in mmol/L (with conversion from mg/dL using the factor 1 mmol/L\u0026thinsp;=\u0026thinsp;38.67 mg/dL); (11) psychiatric rating scores, including the total score or key subscale scores from validated, standardized scales used to assess the severity of mental illness symptoms or overall functioning.\u003c/p\u003e\n \u003cp\u003eExclusion criteria included: (1) reviews, letters, conference abstracts, or case reports; (2) RCTs with unclear outcome measures; (3) RCTs based on different stages of the same group of patients; (4) cross-over design studies; (5) non-inferiority trials comparing metformin/GLP-1 RAs with other non-placebo medications. Before inclusion, RCTs were screened by title and abstract. All included RCTs were double-checked by two reviewers to ensure that the data were from the most recent publications.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.4 Screening Process and Data Extraction\u003c/h2\u003e\n \u003cp\u003eThe retrieved database records were imported into EndNote 20.4.1 (Clarivate Analytics, Philadelphia, PA, USA) to remove duplicates, and the results were combined with those from other sources. The screening process was conducted in three stages. First, three reviewers (Yang QW, Dong YY, and Zhang L) independently selected articles based on titles, including any uncertain entries. Second, all articles selected in the first stage were reviewed in detail, with discrepancies resolved through discussions among the reviewers, and a fourth reviewer (Chen YX) was consulted when necessary. Third, the full texts of articles that met the inclusion and exclusion criteria based on their titles and abstracts were further examined. For each eligible study, a pre-designed form was used to independently extract the following information: study characteristics (publication year, country, treatment duration), population (age, gender, sample size), interventions (name and dosage), and outcomes. Data extraction was performed by two independent reviewers (Chen YX and Yang QW), with subsequent verification and arbitration by a third reviewer (Lin MX)(14, 15). The changes were measured from baseline in outcomes including BMI, WC, BP, FBG, HbA1c, HDL-C, LDL-C, TC, TG, and psychiatric rating scores. Given the significant heterogeneity in psychological assessment tools across studies, and the frequent use of multiple scales (e.g., Positive and Negative Syndrome Scale, PANSS; Brief Psychiatric Rating Scale, BPRS; Clinical Global Impression, CGI; Young Mania Rating Scale, YMRS) within the same study, we prioritized extracting and combining the total scores from standardized scales specifically focused on assessing core schizophrenia symptoms. The primary psychiatric symptom data used in the network meta-analysis were derived from the PANSS total score, MATRICS Consensus Cognitive Battery (MCCB) composite score, and BPRS total score. These outcomes were primarily extracted as mean changes and standard deviations (SD) from baseline to follow-up. When only baseline and endpoint data were reported, the mean change and SD were calculated using the following formula(16, 17). If the relevant correlation coefficient (r) was not reported and could not be inferred, we assumed r\u0026thinsp;=\u0026thinsp;0.5 to calculate the values and assess the robustness of the conclusions(18).\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv format=\"TEX\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1777063247.png\" style=\"width: 478px;\"\u003e\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equb\"\u003e\u003cbr\u003e\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.5 Quality Assessment\u003c/h2\u003e\n \u003cp\u003eThe Cochrane Risk of Bias Tool (version 2.0) (19)was used to assess the risk of bias in the included trials across five domains: randomization, deviations from the intended interventions, missing data, outcome measurement, and selection of reported results. If all domains had a low risk of bias, the overall risk for each trial was considered \u0026quot;low.\u0026quot; If any domain had a high risk of bias, the overall risk was considered \u0026quot;high.\u0026quot; In other cases, the risk of bias was classified as \u0026quot;some concerns.\u0026quot; The risk of bias assessment was performed independently by two reviewers, and any discrepancies were resolved through consensus.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.6 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eA network meta-analysis was conducted using Stata 17.0 MP. For continuous outcome variables with consistent units, the mean difference (MD) and its 95% confidence interval (CI) were used. For psychiatric symptom scores assessed with different ratings, the standardized mean difference (SMD) and its 95% CI were calculated. The primary analysis was performed using a random-effects model under the consistency assumption, with the between-study variance (\u0026tau;\u0026sup2;) estimated using the Restricted Maximum Likelihood (REML) method(20). If a closed-loop structure existed in the network, global inconsistency tests were used to assess consistency, along with local inconsistency tests using the node-splitting method(21). A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 was considered indicative of potential inconsistency. The inconsistency factor (IF) was used to evaluate the consistency of closed loops (on the log scale of effects). If the 95% CI of IF included 0, no statistical evidence of inconsistency between direct and indirect evidence was found. If no closed-loop inconsistency was present, the consistency model was used for analysis. A network graph was created to visualize the geometric structure of the network, where node size was proportional to the total sample size of each treatment, and the thickness of the connecting lines represented the number of studies comparing two interventions. To rank the interventions, multiple ranking metrics were applied, including the surface under the cumulative ranking curve (SUCRA)(22), the probability of being the best treatment (PreBest), and the mean rank(23), to enhance the robustness and interpretability of the results(24). Publication bias and small sample effects were assessed using a comparison-adjusted funnel plot (for networks with more than 10 studies)(23). Sensitivity analysis was performed using the leave-one-out method, by excluding each individual study one at a time, and comparing the direction and magnitude of the combined effects from the random-effects consistency model(25, 26). Further, a univariate network meta-regression was conducted to explore the impact of study-level covariates on treatment effects. Covariates such as intervention time, study location (country/region), mean age of participants, type of mental illness, and whether structured lifestyle interventions were included were analyzed. Since lifestyle interventions are a common part of metabolic management and many studies encouraged patients to modify their lifestyle alongside trial medications, this factor was not distinguished in the network meta-analysis. To assess its impact, we performed a regression analysis based on whether the study included structured lifestyle interventions, to explore its potential moderating effect on treatment outcomes. The regression coefficients, 95% CIs, and Wald test p-values were reported. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to provide statistical evidence of a moderating effect.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.7 GRADE Grading\u003c/h2\u003e\n \u003cp\u003eThe quality of the network meta-analysis results was evaluated using the GRADE framework(27) and the CINeMA (Confidence in Network Meta-analysis) tool(28). Randomized controlled trials were initially rated as \u0026quot;high certainty.\u0026quot; The certainty of evidence was determined by assessing six domains: study-level bias, indirectness, imprecision, heterogeneity, inconsistency, and publication bias/small sample effects(29, 30). Study-level bias was assessed using the RoB 2.0 tool for each domain, and the bias risk was weighted according to its contribution to the network estimate using the CINeMA contribution matrix. Indirectness was assessed based on assumptions of transitivity and exchangeability, with potential effect modifiers predefined to compare the consistency between direct and indirect evidence for population, intervention, control, and outcome measurements. Imprecision was assessed by comparing the effect estimates\u0026apos; 95% CI with the predefined minimal clinical important difference (MID). For continuous outcomes, we defined a SMD of 0.5 as the MID threshold and evaluated whether the 95% CI for effect estimates crossed the null value and this clinical threshold. Heterogeneity was assessed based on\u0026tau;\u003csup\u003e2\u003c/sup\u003e estimated by the random-effects model and the position of the prediction interval relative to the MID. For networks with closed loops, inconsistency was evaluated using CINeMA\u0026apos;s built-in methods (node-splitting or design-treatment interaction models) to assess the consistency between direct and indirect evidence. Publication bias was assessed based on trial registration and grey literature searches, and small sample effects were evaluated using comparison-adjusted funnel plots. Each domain was graded as \u0026quot;no concerns,\u0026quot; \u0026quot;some concerns,\u0026quot; or \u0026quot;serious concerns,\u0026quot; and the evidence certainty was downgraded according to the GRADE principles. If concerns were present, the level was downgraded by one level; if serious concerns were found, the level was downgraded by two levels. The final evidence certainty was categorized as high, moderate, low, or very low.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3 Result","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Characteristics of Included Trials\u003c/h2\u003e \u003cp\u003eIn the initial literature search, a total of 9,588 records were retrieved from the databases (8,290 after removing duplicates). After screening the abstracts to exclude duplicates and irrelevant articles, 212 studies were deemed eligible for full-text review. Ultimately, 29 studies met our inclusion criteria(\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35 CR36 CR37 CR38 CR39 CR40 CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) A total of 1,761 patients were enrolled in the trials, receiving four interventions: metformin, semaglutide, liraglutide, and exenatide. The included studies were published between 2006 and 2025, with the majority conducted in countries such as China, the United States, Denmark, Australia, Iran, and Venezuela. The average age of participants varied widely, covering adolescents to middle-aged adults. Regarding the underlying psychiatric conditions, most patients had schizophrenia or schizoaffective disorder (as diagnosed by DSM-IV/DSM-V or ICD-10), with some studies including bipolar disorder, first-episode psychosis, or autism spectrum disorders. All participants received antipsychotic treatment, such as clozapine and olanzapine. All trials included a placebo-controlled group, with intervention durations ranging from 12 to 40 weeks. Overall, the included studies exhibited some heterogeneity in disease types, intervention drugs, dosage regimens, and geographic locations, but all used a randomized controlled design, providing a solid data foundation for this meta-analysis. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of Included Trials.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStudy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePrimary diagnosis of\u003c/p\u003e \u003cp\u003epsychiatric disorder\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBackground antipsychotic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDuration (weeks)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExperimental regimen (dose, frequency)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eNumbers\u003c/p\u003e \u003cp\u003e(n)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eIntervention\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eControl\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDan Siskind, 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous semaglutide, titrated to 2.0 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAshok A. Ganeshalingam, 2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia, schizotypal disorder, or schizoaffective disorder (ICD-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous semaglutide, titrated to 1.0 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e76\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarie R. Sass, 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia spectrum disorder (ICD-10 or DSM-V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine or olanzapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSemaglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous semaglutide, titrated to 1.0 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSusan L. McElroy, 2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBipolar disorder type I or II (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily subcutaneous liraglutide, titrated to 3.0 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTiannan Shao, 2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia (DSM-V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1500 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCharmaine Tang, 2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSingapore\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode psychosis including schizophrenia, schizoaffective disorder, etc. (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1500 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSri Mahavir Agarwal, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCanada\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia, schizoaffective disorder, or bipolar disorder (DSM-V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1500 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClare A. Whicher, 2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUK\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia, schizoaffective disorder, or first-episode psychosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily subcutaneous liraglutide, titrated to 3.0 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChristoph U. Correll, 2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia spectrum disorder, bipolar spectrum disorder, or psychotic depression (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 500\u0026ndash;1000 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJulie R Larsen, 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia spectrum disorde (ICD-10 or DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine or olanzapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLiraglutide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily subcutaneous liraglutide, titrated to 1.2\u0026ndash;1.8 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP. L. Ish\u0026oslash;y, 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (ICD-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous exenatide, 2 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDan J Siskind, 2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e/\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAustralia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous exenatide, 2 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePelle L Ish\u0026oslash;y, 2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDenmark\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (ICD-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eExenatide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eWeekly subcutaneous exenatide, 2 mg weekly\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEvdokia Anagnostou, 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAutism Spectrum Disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily Oral metformin liquid, titrated to 500\u0026ndash;850 mg twice daily.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJeffrey Rado, 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia, schizoaffective disorder, bipolar disorder, major depression with psychotic features (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOlanzapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily Oral metformin, titrated to 2000 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR-R Wu, 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 500 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChih-Chiang Chiu, 2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 500 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParia Hebrani, 2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIran\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia (DSM-IV-TR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOral metformin 500 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChun-Hsin Chen, 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOral metformin 500 mg three times daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL. Fredrik Jarskog, 2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia or schizoaffective disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1000 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan Wang, 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 500 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRen-Rong Wu, 2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1000 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdgardo Carrizo, 2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVenezuela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia, Bipolar I Disorder and Schizophreniform Disorder (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClozapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 1000 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRen-Rong Wu, 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOral metformin 250 mg three times daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRen-Rong Wu, 2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChina\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOlanzapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eOral metformin 250 mg three times daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrino Baptista, 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVenezuela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFirst-episode schizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin 850\u0026ndash;2550 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrino Baptista, 2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVenezuela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSchizophrenia (DSM-IV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFluphenazine decanoate, levomepromazine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin 850\u0026ndash;2550 mg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDavid J. Klein, 2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUSA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBipolar disorder, attentional disorders, schizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eVarious antipsychotics (unspecified)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin, titrated to 850 mg twice daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrino Baptista, 2006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eVenezuela\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSevere schizophrenia or schizoaffective disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOlanzapine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMetformin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePlacebo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eDaily oral metformin 850\u0026ndash;1700 mg daily\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 ROB2 Literature Quality Assessment\u003c/h2\u003e \u003cp\u003eUsing the ROB2.0 tool for quality assessment, 17 of the 29 included studies were rated as low risk, while 12 were classified as having some concerns. (\u003cb\u003eFigure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Results of network meta-analysis with different outcomes\u003c/h2\u003e \u003cp\u003eFor BMI as the outcome, a total of 27 studies involving 1,625 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) Compared to placebo, Semaglutide (MD = -3.55, 95% CI: -4.27 to -2.84), Liraglutide (MD = -1.56, 95% CI: -2.33 to -0.79), and Metformin (MD = -1.12, 95% CI: -1.44 to -0.80) all significantly reduced BMI. Semaglutide also demonstrated a significant advantage over Liraglutide (MD = -1.99, 95% CI: -3.05 to -0.94), Exenatide (MD = -2.37, 95% CI: -3.88 to -0.85), and Metformin (MD = -2.43, 95% CI: -3.22 to -1.65) in terms of BMI reduction, highlighting its important advantages. The SUCRA values indicated that the efficacy ranking of the four medications in reducing BMI was Semaglutide (99.9%), Liraglutide (63%), Exenatide (45.8%), and Metformin (40.1%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor WC as the outcome, a total of 20 studies involving 1,114 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA) Semaglutide (MD = -6.34, 95% CI: -8.17 to -4.51), Liraglutide (MD = -3.82, 95% CI: -5.42 to -2.23), and Metformin (MD = -1.12, 95% CI: -1.44 to -0.80) significantly reduced WC compared to placebo. Similarly, Semaglutide demonstrated a significant effect in reducing waist circumference compared to Liraglutide (MD = -2.51, 95% CI: -4.94 to -0.09), Exenatide (MD = -3.92, 95% CI: -7.57 to -0.27), and Metformin (MD = -5.05, 95% CI: -7.08 to -3.02), indicating its efficacy in weight loss. The SUCRA values indicated the efficacy ranking of the four medications in reducing WC as follows: Semaglutide (99%), Liraglutide (70.2%), Exenatide (47.4%), and Metformin (31.5%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor HbA1c as the outcome, 15 studies comprising 931 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) Semaglutide (MD = -0.44, 95% CI: -0.53 to -0.35), Exenatide (MD = -0.24, 95% CI: -0.39 to -0.09), Liraglutide (MD = -0.23, 95% CI: -0.32 to -0.15), and Metformin (MD = -0.08, 95% CI: -0.14 to -0.03) all significantly lowered HbA1c compared to placebo. Semaglutide also showed a significant effect on HbA1c reduction compared to Exenatide (MD = -0.20, 95% CI: -0.37 to -0.02), Liraglutide (MD = -0.20, 95% CI: -0.33 to -0.08), and Metformin (MD = -0.35, 95% CI: -0.46 to -0.25). Liraglutide (MD = -0.15, 95% CI: -0.25 to -0.05) significantly reduced HbA1c compared to Metformin, while Exenatide (MD = -0.16, 95% CI: -0.32 to 0.00) approached significance. These findings indicated clear efficacy of these medications for weight loss. The SUCRA values illustrated the ranking of the four medications in lowering HbA1c: Semaglutide (99.7%), Exenatide (62.8%), Liraglutide (61.7%), and Metformin (25.7%), all outperforming placebo. The outstanding efficacy of GLP-1 agonists in glycemic control and weight reduction warranted further attention. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor FBG as the outcome, 15 studies with 1,534 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) Semaglutide (MD = -0.53, 95% CI: -0.88 to -0.18) and Metformin (MD = -0.21, 95% CI: -0.37 to -0.06) significantly lowered FBG compared to placebo. The SUCRA values indicated the efficacy ranking of the four medications in reducing FBG as follows: Semaglutide (84.5%), Exenatide (68.4%), Liraglutide (47%), and Metformin (43.9%), all superior to placebo. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor TC as the outcome, 16 studies involving 1,153 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) Only Metformin (MD = -0.31, 95% CI: -0.59 to -0.03) demonstrated a significant reduction compared to placebo, while other medications showed a reducing trend but did not achieve statistical significance. The SUCRA rankings for these medications compared to placebo were as follows: Metformin (78.1%), Liraglutide (69.1%), Exenatide (48.8%), Placebo (29.1%), and Semaglutide (24.9%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor TG as the outcome, 20 studies including 1,261 participants were analyzed. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC) Only Metformin (MD = -0.16, 95% CI: -0.31 to -0.01) showed a significant reduction compared to placebo, while Exenatide and Liraglutide demonstrated a reducing trend without achieving statistical significance. The SUCRA rankings for these medications compared to placebo were as follows: Metformin (79.9%), Liraglutide (63.7%), Semaglutide (38.7%), Placebo (34%), and Exenatide (33.8%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor the outcome of HDL-C, a total of 19 studies involving 1,236 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) All four medications showed an increasing trend in HDL-C levels compared to the placebo, though none reached statistical significance (Exenatide: 0.08; Liraglutide: 0.08; Metformin: 0.02; Semaglutide: 0.02). The SUCRA rankings for the four medications were as follows: Exenatide (74.9%), Liraglutide (72.6%), Metformin (42.6%), and Semaglutide (40.9%), all demonstrating superiority over the placebo. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor the outcome of LDL-C, 14 studies involving 1,031 participants were included. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) Three medications exhibited a decreasing trend in LDL-C levels compared to the placebo but did not achieve statistical significance (Liraglutide: -0.11; Metformin: -0.25; Semaglutide: -0.06). The SUCRA values ranked as follows: Metformin (76.5%), Liraglutide (53.4%), Semaglutide (45.8%), and Exenatide (42%), all surpassing the placebo. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eFor the outcome of blood pressure, 13 studies involving 697 participants were considered. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE) None of the different medications achieved statistical significance in analyses for reducing SBP and DBP. The SUCRA rankings for SBP were as follows: Exenatide (87.5%), Metformin (67.6%), Semaglutide (58.3%), and Liraglutide (19.5%), all outperforming the placebo. Similarly, the SUCRA rankings for DBP were Exenatide (83.6%), placebo (50.9%), Semaglutide (45.2%), Metformin (41.3%), and Liraglutide (29.1%), all showing better results than the placebo. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eRegarding psychiatric scale scores, 12 studies encompassing 675 participants were analyzed. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF) Compared to the placebo, Metformin demonstrated a significant effect in lowering mental scale scores (SMD = -0.35, 95% CI: -0.61 to -0.09). The SUCRA rankings from highest to lowest were as follows: Metformin (93.9%), placebo (42.4%), Exenatide (42.3%), Liraglutide (38.8%), and Semaglutide (32.7%). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eFor all outcome measures, a sensitivity analysis was performed using the leave-one-out method to assess the influence of individual studies on the network effect estimates. In each round of analysis, one study was excluded, and the remaining studies were re-analyzed using a random-effects consistency network meta-analysis. The results showed that for HbA1c, after excluding certain studies, the significance of semaglutide, metformin, and placebo comparisons disappeared, but the direction of the combined effect remained consistent. For TC, TG, and psychiatric rating scores, the direction of effect remained consistent, although significance was lost after excluding a few studies. This might be related to the limited research available on some GLP-1RAs. For the majority of other sensitivity analyses, the direction of the combined effect remained consistent, the change in effect size was minimal, confidence intervals were highly overlapping, and there were no substantial changes in statistical significance, indicating that the results were robust. (\u003cb\u003eTable S2\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Meta-Regression Analysis\u003c/h2\u003e \u003cp\u003eFor the 11 outcome measures mentioned above, univariate network meta-regression analyses were performed with five covariates, to explore whether these factors influenced the relative effects of different drug interventions. The regression analysis results showed that, for the outcome of HbA1c change, a statistically significant regression coefficient was found when comparing exenatide with liraglutide, with the mean age as a covariate. This suggests that, in this comparison, the baseline age of the patients may have moderated the treatment effect. Additionally, for all other outcome measures and the five covariates, no statistically significant associations were found. This result supports the robustness of the primary conclusions of the study across different clinical contexts. It is worth noting that due to the limited number of studies involving exenatide and liraglutide, we were unable to conduct further subgroup analyses to better explore this moderating effect. (\u003cb\u003eTable S3\u003c/b\u003e)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Bias Assessment and Evidence Grading\u003c/h2\u003e \u003cp\u003eFunnel plots were constructed for all outcome measures to assess publication bias(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). (\u003cb\u003eFig S3\u003c/b\u003e) The distribution of study points was approximately symmetric, with no noticeable outliers, suggesting a low likelihood of publication bias in this study. Using the CINeMA framework, the evidence quality for all outcome measures was assessed. The results revealed differences in the evidence grading across the outcome measures. (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThe management of AIMD remains a core challenge in psychiatric clinical practice, as it requires balancing rehabilitation with safety(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Recently, the widespread use and favorable safety profile of metformin have been recognized as foundational in addressing this issue. The World Health Organization (WHO) published the \"Management of physical health conditions in adults with severe mental disorders\" in 2018, which suggests the adjunctive use of metformin when interventions and/or changes in antipsychotic medications are ineffective(\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e). More recently, GLP-1 RAs have demonstrated remarkable efficacy in treating metabolic disorders, progressively becoming a valuable adjunct in managing AIMD effects, showing distinct advantages. Despite several recent systematic reviews and network meta-analyses evaluating the value of interventions for AIMD, including various medications, some of these have been withdrawn from clinical use due to safety concerns(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Additionally, these analyses have primarily focused on weight changes as the main outcome, failing to comprehensively assess the impact of multidimensional parameters such as blood lipids, blood pressure and psychiatric symptoms. With the growing evidence supporting the use of GLP-1 RAs in metabolic health, recent high-quality RCTs have been conducted since 2025, specifically exploring their effects in populations with psychiatric disorders. This study, through network meta-analysis, for the first time systematically compared the effects of metformin with different GLP-1 RAs (semaglutide, liraglutide, exenatide) on multiple outcome indicators and psychiatric symptoms in patients under antipsychotic treatment. By constructing a more clinically relevant and comprehensive psychiatric evaluation framework, this research provided direct and higher-level evidence for the development of individualized treatment strategies that balance both health and stability.\u003c/p\u003e \u003cp\u003eFirst-generation antipsychotics (FGAs) exert their therapeutic effects primarily through antagonism of dopamine D2 receptors. In contrast, second-generation antipsychotics (SGAs), which have emerged over the past three decades, generally combine D2 receptor antagonism or partial agonism with 5-HT\u003csub\u003e2\u003c/sub\u003eA receptor antagonism and, in some cases, 5-HT\u003csub\u003e1\u003c/sub\u003eA receptor agonism(\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). The disturbances in glucose and lipid metabolism induced by these agents represent a complex pathological process involving bidirectional interactions between the central nervous system and multiple peripheral organs. The central mechanisms underlying AIMD are thought to originate from cascade reactions triggered by blockade of a broad network of brain receptors, ultimately producing widespread perturbations of the energy homeostasis regulatory system. As the principal integrative center for energy balance, the hypothalamus is considered a primary target of antipsychotic action. The drug disrupts the balance of feeding-regulating neurons by antagonizing the 5-HT\u003csub\u003e2\u003c/sub\u003eC and histamine H1 receptors in key hypothalamic nuclei such as the arcuate nucleus (ARC) and the periventricular nucleus (PVH). Consequently, the activity of anorexigenic pro-opiomelanocortin (POMC) neurons is suppressed(\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e), whereas orexigenic agouti-related peptide/neuropeptide Y (AgRP/NPY) neurons are activated, shifting hypothalamic neuropeptide output toward a hunger-promoting profile(\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e). In addition, blockade of histamine H1 receptors has been associated with aberrant activation of AMP-activated protein kinase (AMPK) signaling(\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e, \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) in the ARC and mediobasal hypothalamus (MBH). The activation of AMPK, which serves as a cellular \"energy sensor,\" is believed to positively regulate appetite(\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e). Antipsychotic effects are not restricted to the hypothalamus but extend to the mesolimbic dopaminergic reward circuitry(\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). By modulating projections from the ventral tegmental area (VTA) to the nucleus accumbens (NAc), food reward valuation and food-seeking motivation are altered(\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Concurrently, integration of gastrointestinal satiety signals by the nucleus tractus solitarius (NTS) in the brainstem may be attenuated(\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e). Collectively, these changes promote hyperphagia, reduce satiety, and increase hedonic feeding, thereby constituting a central origin and key driver of antipsychotic-associated dysregulation of glucose and lipid metabolism.\u003c/p\u003e \u003cp\u003eBeyond central dysregulation of appetite control, peripheral effects induced by antipsychotics are widespread and direct. In the pancreas, α- and β-cells are capable of synthesizing dopamine (DA) and maintaining glycemic homeostasis through autocrine/paracrine signaling. By antagonizing dopamine receptors expressed on these cells, antipsychotics are proposed to interrupt this critical local modulatory pathway, thereby directly disrupting islet hormone secretion and glycemic control(\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e). In the liver, antipsychotics have been reported to promote the expression of key gluconeogenic genes via activation of the PI3K\u0026ndash;AKT\u0026ndash;FOXO1 signaling axis(\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), contributing to fasting hyperglycemia. In parallel, hepatic metabolic programs may be coordinately reconfigured through upregulation of TCF7L2, Chrm3, ALK, and the regulatory factor Ptprz1(\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e), thereby enhancing de novo lipogenesis and exacerbating dyslipidemia(\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). In adipose tissue, antipsychotic-induced leptin resistance has been implicated as an important contributor to fat accumulation and metabolic impairment(\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e). Moreover, abnormal fluctuations in adiponectin\u0026mdash;an insulin-sensitizing adipokine\u0026mdash;may further disrupt metabolic crosstalk between adipose tissue and central/peripheral organs. A substantial body of evidence also indicates that antipsychotics can stimulate SREBP-mediated lipogenesis through both direct and indirect mechanisms(\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). In skeletal muscle, insulin resistance is aggravated through interference with the PI3K/Akt\u0026ndash;GLUT4 insulin signaling axis(\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e), accumulation of intracellular lipotoxic metabolites, and impairment of mitochondrial energy metabolism(\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the setting of this multi-pathway dysregulation, metformin has demonstrated distinctive value as a foundational antihyperglycemic agent with multi-organ, integrated regulatory effects. In the present study, metformin was shown to significantly improve multiple metabolic endpoints compared with placebo. Notably, with respect to lipid-related outcomes, the available evidence suggests that metformin may be the only intervention achieving statistically significant improvements in TC and TG. These benefits appear to be mediated primarily through modulation of AMPK, a core cellular energy sensor. In peripheral tissues such as the liver and skeletal muscle, AMPK activation by metformin is associated with reductions in blood glucose(\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e). Downstream cascades include inhibition of acetyl-CoA carboxylase (ACC) with enhanced fatty-acid oxidation(\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e), as well as suppression of key gluconeogenic enzyme expression, thereby reducing hepatic glucose output(\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). Given that antipsychotics can promote SREBP-dependent lipogenesis, metformin has been proposed to counteract antipsychotic-induced lipid droplet accumulation by activating AMPK and suppressing SREBP1 and its downstream targets(\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). This mechanism is consistent with our meta-analytic findings indicating a relative advantage of metformin in improving lipid metabolism. Beyond its canonical effects on insulin signaling and fatty-acid oxidation, metformin has also been reported to markedly regulate hepatic gene programs governing lipid synthesis and clearance. Experimental evidence indicates that, in hepatocytes, PCSK9 expression can be suppressed and SREBP2 downregulated by metformin, leading to increased LDL receptor (LDLR) abundance and enhanced LDL-cholesterol (LDL-C) clearance(\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e). In animal and cellular models of antipsychotic-induced hepatic lipid deposition, expression of lipogenic genes (e.g., PCSK9, FAS, and SCD1) has been reduced by metformin, whereas PPARα and fatty-acid β-oxidation\u0026ndash;related genes have been restored, thereby markedly reversing intrahepatic lipid accumulation(\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e). Interestingly, hypothalamic actions of metformin appear to be mechanistically distinct. Metformin has been reported to cross the blood\u0026ndash;brain barrier and to limit excessive increases in appetite by decreasing NPY and AgRP mRNA expression and activating STAT3 signaling. Notably, this process has been described as occurring without hypothalamic AMPK activation(\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e). Earlier studies further suggested that AMPK activity in hypothalamic neurons can be inhibited by metformin, thereby modulating expression of the orexigenic peptide NPY(\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e). Collectively, through multidimensional mechanisms, metformin may serve as a cornerstone therapy capable of comprehensive and early intervention for antipsychotic-associated dysregulation of glucose and lipid metabolism.\u003c/p\u003e \u003cp\u003eThis study also indicated that GLP-1 RAs provide uniquely robust efficacy for glycemic control and weight reduction in patients with AIMD, with greater improvements than metformin in BMI, WC, HbA1c, and FBG. GLP-1 is an incretin hormone secreted primarily by intestinal L cells, and GLP-1 RAs are glucose-lowering agents that mimic its physiological actions. In a mouse model of long-term olanzapine exposure, GLP-1 receptor expression in β cells was reported to be markedly reduced, with concomitant worsening of glucose dysregulation. Moreover, mice with conditional deletion of the Wnt-pathway transcription factor Tcf7l2 were more susceptible to olanzapine-induced metabolic abnormalities. Together, these findings suggest that regulation of the GLP-1 axis may influence vulnerability to antipsychotic-associated metabolic injury. Within the central nervous system, GLP-1 RAs are thought to directly activate hypothalamic satiety circuits and enhance pro-opiomelanocortin (POMC) neuronal activity, producing a strong and sustained sensation of satiety(\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e). More recent work has suggested complex interactions between GLP-1 receptor\u0026ndash;expressing neurons in the dorsomedial hypothalamus (DMH) and ARC neuropeptide Y (NPY)/agouti-related peptide (AgRP) neurons, jointly shaping the regulation of food intake(\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e). In addition to suppressing hunger at the hypothalamic level, GLP-1 RAs have also been reported to act on mesolimbic reward circuits (e.g., VTA/NAc), directly modulating dopaminergic neurotransmission and attenuating food reward(\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e). This effect conceptually align with mechanisms implicated in antipsychotic-induced hyperphagia. Beyond potent central appetite suppression, weight loss and metabolic improvement are further supported by multiple peripheral mechanisms, including delayed gastric emptying, enhanced glucose-dependent insulin secretion, suppression of glucagon release, reduced adipose storage, and attenuation of inflammatory responses. With the rapid expansion of GLP-1 RA use in recent years, these glucose-lowering and weight-reducing mechanisms have been extensively investigated(\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOwing to their strong effects on weight reduction and glycemic control, GLP-1 RAs may offer a more efficient therapeutic option for patients with AIMD and could be prioritized in selected individuals. However, the mechanisms and clinical effects of GLP-1 RAs on mental health outcome remain controversial. Some clinical studies and systematic reviews have suggested potential antidepressant benefits(\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e), whereas multiple observational and cohort studies have raised concerns. In several large real-world datasets, GLP-1 RA use has been reported to be positively associated with the risk of psychiatric disorders, including major depression and anxiety(\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). In addition, case reports and pharmacovigilance-database analyses have linked GLP-1 RAs to mood worsening, suicidal ideation, or other psychiatric symptoms in a subset of patients(\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e). Consequently, concern persists in clinical practice regarding the potential for psychiatric symptom exacerbation, particularly among individuals with a prior psychiatric history or elevated vulnerability to mood disorders. These uncertainties highlight the need for larger, rigorously designed randomized controlled trials and mechanistic studies to clarify the true impact of GLP-1 RAs on mental health and to provide a more reliable evidence base for clinical decision-making.\u003c/p\u003e \u003cp\u003eIn the present study, a favorable trend was observed for metformin in improving schizophrenia-related rating scale scores, suggesting potential psychiatric benefits beyond metabolic regulation alone. Such multidimensional symptom improvement may be attributable to complex, synergistic, and multi-target actions of metformin within the central nervous system. First, brain energy homeostasis may be directly improved. Patients with schizophrenia frequently exhibit abnormal cerebral glucose metabolism and mitochondrial dysfunction, particularly in the prefrontal cortex and hippocampus(\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). Metformin has been reported to cross the blood\u0026ndash;brain barrier and to activate AMPK, thereby systemically modulating energy metabolism(\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). Evidence indicates that metformin can normalize peripheral abnormalities in tricarboxylic acid (TCA) cycle\u0026ndash;related metabolites in patients with schizophrenia(\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e), including reductions in lactate and increases in citrate and succinate levels. These changes suggest improved mitochondrial oxidative metabolism efficiency and a greater supply of energy substrates required for synaptic plasticity and high-demand neuronal activity. Notably, such metabolic remodeling has been significantly associated with improvements in working memory and verbal learning. Second, neurohealth may be indirectly promoted through anti-inflammatory and antioxidative mechanisms. Chronic low-grade inflammation and oxidative stress are key pathophysiological components of major depressive disorder(\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e) and schizophrenia, and can damage neurons and impede neuroplasticity. Excessive microglial activation has been shown to be suppressed by metformin, accompanied by reduced release of proinflammatory cytokines and enhancement of endogenous antioxidant defenses(\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e). In a methamphetamine-induced neurotoxicity model, metformin exhibited clear neuroprotective effects, alleviating oxidative stress and hippocampal inflammation. These effects were closely linked to modulation of the Akt/GSK3β signaling pathway(\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). More importantly, neuroplasticity and functional connectivity may be enhanced by metformin. Impaired neuroplasticity\u0026mdash;particularly downregulation of brain-derived neurotrophic factor (BDNF) signaling involved in learning and memory\u0026mdash;has been recognized as a core mechanism underlying cognitive deficits in schizophrenia. Through activation of the AMPK/CREB pathway, BDNF expression in the hippocampus and other regions has been reported to be upregulated by metformin. Functional magnetic resonance imaging studies further showed that, following adjunctive metformin treatment, functional connectivity was strengthened between the right caudate/hippocampus and the ventral occipital cortex as well as the middle frontal gyrus(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Because these regions are implicated in memory formation, visual information processing, and working memory, enhanced connectivity provides a systems-level circuit explanation for the observed improvements in cognitive performance scores.\u003c/p\u003e \u003cp\u003eOn the basis of our findings and the existing evidence, management of AIMD should be guided by an integrated, proactive, and individualized strategy. The starting point should be earlier monitoring and continuous, longitudinal oversight. To achieve this shift from reactive treatment to proactive prevention(\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e), collaborative care models should be established between psychiatrists and endocrinologists or primary-care clinicians, with metabolic parameters incorporated into routine psychiatric follow-up. With respect to pharmacotherapy, more precise selection and targeted intervention should be implemented. When a single metabolic abnormality predominates, agent selection may be guided by the comparative findings of this study. However, given that multiple metabolic disturbances frequently coexist, combination therapy with metformin plus a GLP-1 RA may represent a preferable option. Further studies are needed to validate potential synergistic effects and to establish long-term safety. Importantly, all management decisions should be anchored in the core principle that metabolic and psychiatric health are equally prioritized(\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e). The ultimate goal is not merely normalization of metabolic indices, but improvement of overall physical health to strengthen treatment confidence and adherence, thereby providing a more stable physiological foundation for psychiatric recovery and ultimately achieving a dual benefit for both physical and mental health.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged, despite the use of a network meta-analysis to compare pharmacologic strategies for AIMD. First, the number of included studies and total sample size remained limited, particularly for drug-specific analyses of individual GLP-1 RAs, which constrained more granular subgroup assessments. Second, because individual participant data were unavailable, baseline confounders could not be further adjusted for, and potential effect modifiers could not be rigorously examined. Third, direct head-to-head comparisons between GLP-1 RAs and metformin were lacking in the current evidence network. Consequently, treatment ranking relied primarily on indirect comparisons. Although the consistency assessment supported the inference, confirmation in future direct comparative trials is required. Fourth, sensitivity analyses indicated that statistical significance for certain outcomes changed after exclusion of individual studies, suggesting that some results may have been influenced by single-study effects. Although the overall direction of effect was preserved, cautious interpretation is warranted. Finally, several included trials had small sample sizes (e.g., Study 6), which may have introduced small-study bias. Future research should prioritize larger, multicenter randomized controlled trials with longer follow-up, particularly those conducting direct comparisons among active agents and collecting individual-level data, to further validate these conclusions and to provide higher-level evidence for precision and personalized clinical management.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis network meta-analysis systematically compared the multidimensional efficacy of metformin and three GLP-1 RAs in improving AIMD. The results indicated that different medications exhibit distinct advantages in improving metabolic parameters, suggesting that clinical choices should be individualized based on specific metabolic abnormalities. This study provides comprehensive evidence for clinicians in managing antipsychotic-induced metabolic risks, emphasizing the importance of considering the potential impact on psychiatric symptoms while striving for improvements in metabolic outcomes. It advocates for the development of a patient-centered, integrated management strategy that balances both metabolic and mental health.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFinancial disclosure statement\u003c/b\u003e: This study was supported by the Fundamental Research Funds for the Central Universities (Grant Nos. 2024-JYB-JBZD-011, 2023-JYB-JBZD-003), and the High-Level Hospital Project of Dongzhimen Hospital, Beijing University of Chinese Medicine (Grant Nos. DZMG-LJRC0001), National Administration of Traditional Chinese Medicine High-Level Key Discipline: Traditional Chinese Medicine Endocrinology (404053503).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict\u003c/h2\u003e \u003cp\u003e \u003cb\u003e-of-interest\u003c/b\u003e: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYe-xin Chen, Qian-wen Yang, Mao-xuan Lin: Co-first authors. Contributed equally to Conceptualization, Methodology, Data curation and validation, Risk of Bias assessment, and Writing - original draft. Mo-han Sun, Yi-yu Dong, Run-dong Yu: Contributed to Literature search, screening, and Data curation. Dan-dan Mao, Yan Zhao: Contributed to Supervision, Resources, and Project administration. Lin Zhang: Contributed to Formal analysis, Data visualization. Jin-xi Zhao, Yao-fu Zhang, Jun Xu: As corresponding authors, contributed to Supervision, Project coordination, and the final review, editing, and submission of the manuscript. All authors have read and approved the final version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRehm J, Shield KD. Global Burden of Disease and the Impact of Mental and Addictive Disorders. Curr psychiatry Rep. 2019;21(2). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.DOI:10.1007/s11920-019-0997-0\u003c/span\u003e\u003cspan address=\"10.DOI:10.1007/s11920-019-0997-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLibowitz MR, Nurmi EL. 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J Affect Disord. 2025;390:119794DOI. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.jad.2025.119794\u003c/span\u003e\u003cspan address=\"10.1016/j.jad.2025.119794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Metformin, GLP-1 Receptor Agonists, Antipsychotic-Induced Metabolic Disturbances, Systematic Review, Network Meta-Analysis","lastPublishedDoi":"10.21203/rs.3.rs-9317100/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9317100/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e The management of antipsychotic-induced metabolic disturbances (AIMD) represents a significant challenge in psychiatric clinical practice. Although metformin is widely used to improve AIMD, the role of glucagon-like peptide-1 receptor agonists (GLP-1 RAs) in metabolic regulation has gained increasing attention. This study aims to compare the efficacy of metformin and different GLP-1 RAs in improving multidimensional metabolic indicators and psychiatric symptoms in AIMD patients through a systematic review and network meta-analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eRandomized controlled trials (RCTs) published up until December 1, 2025, were identified by searching PubMed, Embase, Cochrane Library, and Web of Science databases. Studies that included patients receiving metformin or GLP-1 RAs treatment for at least 12 weeks, while continuously using antipsychotic medications, were included. The Cochrane Risk of Bias 2.0 tool was used to assess the quality of the studies. A random-effects network meta-analysis was performed using Stata 17.0 MP within the frequentist framework. Intervention rankings were determined by calculating the surface under the cumulative ranking curve (SUCRA). Univariate network meta-regression was applied to explore the impact of study-level covariates on treatment efficacy. Evidence quality was rated based on the CINeMA framework.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eA total of 29 RCTs (1,761 patients) were included. Semaglutide demonstrated the most significant effect in reducing body mass index (BMI) (MD = -3.55, 95% CI: -4.27 to -2.84). It also showed the best results in reducing waist circumference (WC) (MD = -6.34, 95% CI: -8.17 to -4.51). Moreover, semaglutide was significantly superior to other interventions in controlling glycated hemoglobin A1c (HbA1c) (MD = -0.44, 95% CI: -0.53 to -0.35) and fasting blood glucose (FBG) (MD = -0.53, 95% CI: -0.88 to -0.18). Additionally, metformin demonstrated a significant advantage over placebo in improving psychiatric symptom scores (SMD = -0.35, 95% CI: -0.61 to -0.09), and showed unique benefits in regulating lipid metabolism markers such as total cholesterol and triglycerides.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eDifferent medications exhibit distinct advantages in managing AIMD across various metabolic indicators. GLP-1 RAs, particularly semaglutide, demonstrate remarkable efficacy in weight loss and glycemic control, while metformin excels in lipid regulation and psychiatric symptom improvement. Clinical decisions should be individualized based on the patient's specific metabolic abnormalities, with a comprehensive consideration of the dual impact of medications on both metabolic and psychiatric symptoms to achieve optimal overall health.\u003c/p\u003e","manuscriptTitle":"Comparative Effectiveness of Metformin versus GLP-1 Receptor Agonists in Treating Antipsychotic-Induced Metabolic Disturbances: A Systematic Review and Network Meta-Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-26 15:37:58","doi":"10.21203/rs.3.rs-9317100/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"139013489162788036333130305699937159831","date":"2026-04-30T00:14:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-24T09:28:43+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T15:22:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"164982857630831523029989574658327165373","date":"2026-04-20T15:20:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"132590676592713444938287082679712673509","date":"2026-04-16T13:34:47+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-16T12:06:50+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-06T07:14:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-06T07:01:21+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Medicine","date":"2026-04-04T02:53:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bmed","sideBox":"Learn more about [BMC Medicine](http://bmcmedicine.biomedcentral.com/)","snPcode":"12916","submissionUrl":"https://submission.nature.com/new-submission/12916/3","title":"BMC Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8a32045-79d2-465e-aceb-1d30a10fc743","owner":[],"postedDate":"April 26th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"139013489162788036333130305699937159831","date":"2026-04-30T00:14:45+00:00","index":25,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-26T15:37:58+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-26 15:37:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9317100","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9317100","identity":"rs-9317100","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}