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Long-term obesity impacts brain morphology, functional connectivity and cognition in adults

Nature Mental Health volume 3, pages 466–478 (2025)Cite this article

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Abstract

Although obesity has been implicated in brain and cognitive health, the effect of longitudinal obesity trajectories on brain and cognitive aging remains insufficiently understood. Here, using multifaceted obesity measurements from the UK Biobank, we identified five distinct obesity trajectories: low-stable, moderate-stable, high-stable, increasing and decreasing. We observed that individuals in the decreasing trajectory showed minimal adverse effects on brain structure and cognitive performance, compared with the low-stable trajectory (low obesity levels over time). By contrast, the increasing and moderate- and high-stable trajectories were associated with progressively greater impairments in brain morphology, functional connectivity and cognitive abilities. Specifically, adverse effects extended from fronto-mesolimbic regions in the increasing trajectory to parietal and temporal regions in the moderate-stable trajectory, culminating in widespread brain abnormalities in the high-stable group. These findings highlight the dynamic relationship between obesity evolution and brain-cognitive health, underscoring the clinical importance of long-term monitoring and management of obesity through a multifaceted approach.

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Fig. 1: Flowchart of the participant selection.
Fig. 2: Longitudinal obesity trajectory analysis.
Fig. 3: Statistical maps of standardized β coefficients for the effects of longitudinal obesity trajectories on cortical thickness (left column), subcortical volume (middle column) and FC (right column).
Fig. 4: Distribution of cognitive scores in each obesity group.

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Data availability

The UK Biobank data are a public health database. This work mainly includes demographic data, multiple obesity measures, brain images and cognitive tests. Researchers can apply for access to the data on the UK Biobank website (https://www.ukbiobank.ac.uk). Additional information regarding registration for data access is available at http://www.ukbiobank.ac.uk/register-apply/. The application number for this study is 57831.

Code availability

No new algorithms were written for this study. Study analyses were carried out in MATLAB R2017b (The MathWorks) and Python 3.6. The version of R software is 4.3.1. The code is available from the corresponding author upon reasonable request.

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Acknowledgements

This research is supported by the STI 2030 – Major Project (project no. 2022ZD0209000), the National Research Foundation, Singapore, and the Agency for Science Technology and Research (A*STAR), Singapore, under its Prenatal/Early Childhood Grant (grant no. H22P0M0007). Additional support is provided by the RGC GRF project (project no. 15201124) and the Hong Kong Global STEM Scholar scheme. We also thank S. Lyu (Department of Biomedical Engineering, National University of Singapore, Singapore) for his initial work on this study.

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Authors and Affiliations

  1. Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Die Zhang & Anqi Qiu

  2. Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

    Chenye Shen, Nanguang Chen, Chaoqiang Liu & Anqi Qiu

  3. NUS (Suzhou) Research Institute, Suzhou, China

    Nanguang Chen

  4. Peking University Shenzhen Hospital, Shenzhen, China

    Jun Hu

  5. School of Clinical Medicine, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong

    Kui Kai Lau

  6. State Key Laboratory of Brain and Cognitive Sciences, The University of Hong Kong, Hong Kong, Hong Kong

    Kui Kai Lau

  7. Department of Radiology, Zhujiang Hospital, Southern Medical University, Guangzhou, China

    Zhibo Wen

  8. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Anqi Qiu

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Contributions

A.Q. led the project. A.Q., D.Z, C.S., C.L. and N.C. were responsible for the study concept and the design of the study. D.Z., C.L. and C.S. contributed to brain image analysis and statistical analysis. C.S., J.H., K.K.L. and Z.W. contributed to visualization. A.Q., D.Z., J.H., K.K.L. and Z.W. contributed to the results interpretation and discussion. All the authors contributed to the manuscript writing.

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Correspondence to Anqi Qiu.

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Nature Mental Health thanks Wyllians Borelli and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Table 1 Statistical comparisons of sample characteristics between the trajectory analysis group and other subgroups
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Extended Data Table 2 Statistical comparisons of sample characteristics of brain morphology analysis between the low-stable group and other groups
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Extended Data Table 3 Statistical comparisons of sample characteristics of brain functional connectivity analysis between the low-stable group and other groups
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Extended Data Table 4 Statistical comparisons of subcortical analysis between the low-stable group and other groups
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Supplementary methods, Figures 1–4 and Tables 1–5.

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Zhang, D., Shen, C., Chen, N. et al. Long-term obesity impacts brain morphology, functional connectivity and cognition in adults. Nat. Mental Health 3, 466–478 (2025). https://doi.org/10.1038/s44220-025-00396-5

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