Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia
Abstract Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Bioma...
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| Format: | Article |
| Language: | English |
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Nature Publishing Group
2025-01-01
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| Series: | Translational Psychiatry |
| Online Access: | https://doi.org/10.1038/s41398-025-03247-0 |
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| author | Caitlin A. Finney David A. Brown Artur Shvetcov |
| author_facet | Caitlin A. Finney David A. Brown Artur Shvetcov |
| author_sort | Caitlin A. Finney |
| collection | DOAJ |
| description | Abstract Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Clinical variables included 21 measures across medical history, hematological and other blood tests, and APOE genotype. Tree-based machine learning algorithms and artificial neural networks were used. APOE genotype was the best predictor of dementia cases and healthy controls. Our results, however, demonstrated that there are limitations when using publicly accessible cohort data that may limit the generalizability and interpretability of such predictive models. Future research should examine the use of routine APOE genetic testing for dementia diagnostics. It should also focus on clearly unifying data across clinical cohorts. |
| format | Article |
| id | doaj-art-0ad9c59754fd4dd49d61e059402044b8 |
| institution | Kabale University |
| issn | 2158-3188 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Publishing Group |
| record_format | Article |
| series | Translational Psychiatry |
| spelling | doaj-art-0ad9c59754fd4dd49d61e059402044b82025-01-26T12:53:43ZengNature Publishing GroupTranslational Psychiatry2158-31882025-01-0115111010.1038/s41398-025-03247-0Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and AustraliaCaitlin A. Finney0David A. Brown1Artur Shvetcov2Translational Dementia Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical ResearchNeuroinflammation Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical ResearchTranslational Dementia Research Group, Centre for Immunology and Allergy Research, Westmead Institute for Medical ResearchAbstract Existing dementia prediction models using non-neuroimaging clinical measures have been limited in their ability to identify disease. This study used machine learning to re-examine the diagnostic potential of clinical measures for dementia. Data was sourced from the Australian Imaging, Biomarkers, and Lifestyle Flagship Study of Ageing (AIBL) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Clinical variables included 21 measures across medical history, hematological and other blood tests, and APOE genotype. Tree-based machine learning algorithms and artificial neural networks were used. APOE genotype was the best predictor of dementia cases and healthy controls. Our results, however, demonstrated that there are limitations when using publicly accessible cohort data that may limit the generalizability and interpretability of such predictive models. Future research should examine the use of routine APOE genetic testing for dementia diagnostics. It should also focus on clearly unifying data across clinical cohorts.https://doi.org/10.1038/s41398-025-03247-0 |
| spellingShingle | Caitlin A. Finney David A. Brown Artur Shvetcov Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia Translational Psychiatry |
| title | Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia |
| title_full | Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia |
| title_fullStr | Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia |
| title_full_unstemmed | Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia |
| title_short | Developing multifactorial dementia prediction models using clinical variables from cohorts in the US and Australia |
| title_sort | developing multifactorial dementia prediction models using clinical variables from cohorts in the us and australia |
| url | https://doi.org/10.1038/s41398-025-03247-0 |
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