Association of GLP-1RA discontinuation and risk of depressive and anxiety disorders in people with type 2 diabetes: a cohort study

Abstract

The psychiatric safety profile of glucagon-like peptide-1 receptor agonists (GLP-1RAs) is characterized by inconsistent findings during active treatment and remains largely unexplored after cessation. Here we show that GLP-1RA discontinuation is associated with an increased risk of psychiatric events. Using large-scale electronic health records from the Shanghai Hospital Link Database, we investigate incident depressive and anxiety disorders during GLP-1RA treatment and after its discontinuation in people with type 2 diabetes, compared with dipeptidyl peptidase-4 inhibitors (DPP4is) or sodium–glucose cotransporter-2 inhibitors (SGLT2is). During treatment, GLP-1RAs show a neutral risk compared with DPP4is and a lower risk compared with SGLT2is. In contrast, after cessation, prior use of GLP-1RAs is associated with a higher risk than prior use of DPP4is or SGLT2is. This increase is modestly mediated by elevated triglyceride levels. These findings highlight the need for clinical vigilance regarding anxiety following GLP-1RA discontinuation.

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Data availability

The clinical data analysed during the current study are not publicly available due to patient confidentiality and regulations governing the Shanghai Hospital Link Database (SHLD), but they are available from the corresponding authors on reasonable request and with institutional approval.ta are available from the corresponding authors on reasonable request

Code availability

Data processing and statistical analyses were performed using the open-source software R (version 4.5.1). The specific packages used included tidyverse (v2.0.0), survival (v3.8-3), MatchIt (v4.7.2), survminer (v0.5.0), cmprsk (v3.1), WeightIt (v1.2.0) and mediation (v4.5.0). The standard analysis scripts used to generate the results are publicly available in the GitHub repository at https://github.com/tyzhang-lab/GLP1RA-Psychiatric-Analysis and archived on Zenodo (https://doi.org/10.5281/zenodo.20439549)²⁸.

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Acknowledgements

We are grateful to the Shanghai Hospital Link Database (SHLD) for providing the electronic health records used in this study

Funding

This work was supported by grants from the National Key R&D Program of China (2022ZD0160700). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the paper

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Author notes

1. These authors contributed equally: Tingyu Zhang, Ping He, Yunxi Ji, Juan Shi

Authors and Affiliations

1. Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

   Tingyu Zhang, Yunxi Ji, Juan Shi, Zizheng Zhang, Kan Wang, Haolei Pan, Ruizhi Zheng, Bin Cui, Yifei Zhang, Weiqing Wang & Guang Ning

2. Shanghai National Clinical Research Center for Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the People’s Republic of China, Shanghai Key Laboratory for Endocrine Tumor, State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

   Tingyu Zhang, Yunxi Ji, Juan Shi, Zizheng Zhang, Haolei Pan, Bin Cui, Yifei Zhang, Weiqing Wang & Guang Ning

3. Link Healthcare Engineering and Information Department, Shanghai Hospital Development Center, Shanghai, China

   Ping He

4. Computer Net Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China

   Huayan Yao & Yanbin Xue

5. Wonders Information Co., Ltd., Shanghai, China

   Qingxia Wu

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Contributions

G.N. and B.C. contributed to the conception and design of the study. P.H., H.Y. and Y.X. contributed to data collection and management. T.Z., R.Z. and K.W. contributed to the statistical methodology. W.W., Y.Z. and J.S. contributed to clinical experience and support. T.Z., Y.J., Z.Z., H.P. and Q.W. performed data extraction, curation and statistical analyses. P.H., Y.X., W.W. and Z.Z. contributed to quality control and support. T.Z., K.W., R.Z., B.C. and G.N. interpreted the findings and contributed to the discussion. B.C., Y.Z., W.W. and G.N. provided supervision. T.Z. and B.C. drafted the initial paper. G.N., R.Z. and K.W. critically revised the paper. All authors contributed to the acquisition or interpretation of data, reviewed the paper for accuracy and intellectual content, and approved the final version for submission. B.C., Y.Z., W.W. and G.N. are the guarantors of this work and take full responsibility for the integrity of the data and the accuracy of the data analysis.

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Nature Metabolism thanks Hamlet Gasoyan, Jason Halford and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Jean Nakhle, in collaboration with the Nature Metabolism team

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Extended data

Extended Data Fig. 1 Study Flow Diagram for Selecting Patients Initiating Glucagon-Like Peptide-1 Receptor Agonist (GLP-1RA) or Dipeptidyl Peptidase-4 Inhibitor (DPP-4i)

Flowchart illustrating the sequential patient selection process for the GLP-1RA and DPP-4i study cohorts from the Shanghai Hospital Link Database (SHLD)

Extended Data Fig. 2 Study Flow Diagram for Selecting Patients Initiating Glucagon-Like Peptide-1 Receptor Agonist (GLP-1RA) or Sodium-Glucose Cotransporter-2 Inhibitor (SGLT-2i)

Flowchart illustrating the sequential patient selection process for the GLP-1RA and SGLT-2i study cohorts from the Shanghai Hospital Link Database (SHLD)

Extended Data Fig. 3 Prespecified Subgroup Analyses of Depressive and Anxiety Disorders (GLP-1RA vs DPP-4i)

Hazard ratios and 95% CIs are shown for prespecified subgroups during the on-treatment and post-treatment periods. For analyses by molecular structure, each individual GLP-1RA type was separately matched 1:1 with DPP-4i comparators using propensity scores. Exact P values for interaction were calculated using two-sided Wald tests for multiplicative interaction terms within the Cox proportional hazards models. No adjustments were made for multiple comparisons.P values for interaction were calculated using multiplicative interaction terms. Data are presented as point estimates of hazard ratios (HRs) as the measure of centre, with error bars defining the 95% confidence intervals (CIs).CI indicates confidence interval; DPP-4i, dipeptidyl peptidase-4 inhibitor; GLP-1RA, glucagon-like peptide-1 receptor agonist; HR, hazard ratio.

Source data

Extended Data Fig. 4 Prespecified Subgroup Analyses of Depressive and Anxiety Disorders (GLP-1RA vs SGLT-2i)

Hazard ratios and 95% CIs are shown for prespecified subgroups during the on-treatment and post-treatment periods. For analyses by molecular structure, each individual GLP-1RA type was separately matched 1:1 with SGLT-2i comparators using propensity scores. Exact P values for interaction were calculated using two-sided Wald tests for multiplicative interaction terms within the Cox proportional hazards models. No adjustments were made for multiple comparisons.P values for interaction were calculated using multiplicative interaction terms. Data are presented as point estimates of hazard ratios (HRs) as the measure of centre, with error bars defining the 95% confidence intervals (CIs). GLP-1RA, glucagon-like peptide-1 receptor agonist; SGLT-2i, sodium-glucose co-transporter-2 inhibitor.

Source data

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Zhang, T., He, P., Ji, Y. et al. Association of GLP-1RA discontinuation and risk of depressive and anxiety disorders in people with type 2 diabetes: a cohort study.
Nat Metab (2026). https://doi.org/10.1038/s42255-026-01567-z

• Received:22 September 2025

• Accepted:11 June 2026

• Published:09 July 2026

• Version of record:09 July 2026

• DOI
  :https://doi.org/10.1038/s42255-026-01567-z