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Latent profiles of modifiable dementia risk factors in later midlife: relationships with incident dementia, cognition, and neuroimaging outcomes

Molecular Psychiatry (2024)Cite this article

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Abstract

In 2020, the Lancet Commission identified 12 modifiable factors that increase population-level dementia risk. It is unclear if these risk factors co-occur among individuals in a clinically meaningful way. Using latent class analysis, we identified profiles of modifiable dementia risk factors in dementia-free adults aged 60–64 years from the UK Biobank. We then estimated associations between these profiles with incident dementia, cognition, and neuroimaging outcomes, and explored the differences across profiles in the effects of a polygenic risk score for Alzheimer’s disease on outcomes. In 55,333 males and 63,063 females, three sex-specific latent profiles were identified: cardiometabolic risk, substance use-related risk, and low risk. The cardiometabolic risk profile in both males and females was associated with greater incidence of all-cause dementia (male: OR [95% CI] = 2.33 [2.03, 2.66]; female: OR [95% CI] = 1.44 [1.24, 1.68]), poorer cognitive performance, greater brain atrophy, and greater white matter hyperintensity volume compared to the low risk profile. The substance use-related risk profile in males was associated with poorer cognitive performance and greater white matter hyperintensities compared to the low risk profile, but no difference in all-cause dementia incidence was observed (OR [95% CI] = 1.00 [0.95, 1.06]). In females, the substance use-related risk profile demonstrated increased dementia incidence (OR [95% CI] = 1.58 [1.57, 1.58]) and greater brain atrophy but smaller white matter hyperintensity volume compared to the low risk profile. The polygenic risk score had larger effects among females, and differentially influenced outcomes across profiles; for instance, there were larger effects of the polygenic risk score on atrophy in the cardiometabolic profile vs. the low risk profile among males, and larger effects of the polygenic risk score on loss of white matter integrity in the cardiometabolic profile vs. the low risk profile among females. These results reveal three modifiable dementia risk profiles, their unique cognitive/neuroimaging outcomes, and their interactions with genetic risk for Alzheimer’s disease. These differences highlight the need to consider population heterogeneity in risk prediction tools and in planning personalized prevention strategies.

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Fig. 1: Risk factor profiles identified in males and females.
Fig. 2: Heatmap showing pairwise differences between risk factor profiles for cognitive and neuroimaging outcomes after correction for multiple comparisons at a 5% false discovery rate.
Fig. 3: Association between the AD PRS and outcomes in each risk factor profile.

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Data availability

Access to UK Biobank data is available to researchers through application on the UK Biobank website (https://www.ukbiobank.ac.uk).

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Acknowledgements

This research has been conducted using data the UK Biobank Resource under Application Number 56197. This work uses data provided by patients and collected by the NHS as part of their care and support. We gratefully thank all the participants of UK Biobank. This work was funded by the Canadian Institutes of Health Research (Doctoral Research Award: Canadian Graduate Scholarship, 202111FBD-476226; Canada Research Chair, CRC-2020-00353; PJT-159711), Natural Sciences and Engineering Research Council of Canada (RGPI N-2017-06962), Alzheimer’s Association and Brain Canada (AARG501466), the Ontario Ministry of Colleges and Universities (ER21-16-141), Weston Brain Institute, Alzheimer’s Research UK, Alzheimer’s Association, and Michael J. Fox Foundation (BAND3). The funders were not involved at any stage of the study.

Author information

Authors and Affiliations

  1. Dr. Sandra Black Centre for Brain Resilience and Recovery, Hurvitz Brain Sciences Program, Sunnybrook Research Institute, Toronto, ON, Canada

    Lisa Y. Xiong, Madeline Wood Alexander, Yuen Yan Wong, Che-Yuan Wu, Myuri Ruthirakuhan, Krista L. Lanctôt, Sandra E. Black, Jennifer S. Rabin & Walter Swardfager

  2. Department of Pharmacology & Toxicology, University of Toronto, Toronto, ON, Canada

    Lisa Y. Xiong, Yuen Yan Wong, Che-Yuan Wu, Krista L. Lanctôt & Walter Swardfager

  3. Rehabilitation Sciences Institute, University of Toronto, Toronto, ON, Canada

    Madeline Wood Alexander & Jennifer S. Rabin

  4. University of Ottawa Heart Institute, Ottawa, ON, Canada

    Jodi D. Edwards

  5. School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada

    Jodi D. Edwards

  6. ICES, Ottawa, ON, Canada

    Jodi D. Edwards

  7. Department of Psychiatry, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Krista L. Lanctôt

  8. Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada

    Sandra E. Black & Jennifer S. Rabin

  9. Department of Neurology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Sandra E. Black

  10. Harquail Centre for Neuromodulation, Sunnybrook Health Sciences Centre, Toronto, ON, Canada

    Jennifer S. Rabin

  11. Department of Education, ICT and Learning, Østfold University College, Halden, Norway

    Hugo Cogo-Moreira

  12. Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada

    Walter Swardfager

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Contributions

LYX, HCM, and WS conceptualized and designed the study. LYX and HCM performed the statistical analysis. LYX, YYW, and CYW were responsible for data acquisition and processing. LYX drafted the manuscript. WS provided supervision. All authors contributed to the interpretation of the data, provided critical revision of the manuscript, and approved the final version of the manuscript.

Corresponding author

Correspondence to Walter Swardfager.

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Xiong, L.Y., Wood Alexander, M., Wong, Y.Y. et al. Latent profiles of modifiable dementia risk factors in later midlife: relationships with incident dementia, cognition, and neuroimaging outcomes. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02685-4

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