MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929483261411328 |
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| author | Mateus, Pedro Garst, Swier Yu, Jing Cats, Davy Harms, Alexander G. J. Birhanu, Mahlet Beekman, Marian Slagboom, P. Eline Reinders, Marcel van der Grond, Jeroen Dekker, Andre Jansen, Jacobus F. A. Beran, Magdalena Schram, Miranda T. Visser, Pieter Jelle Moonen, Justine Ghanbari, Mohsen Roshchupkin, Gennady Vojinovic, Dina Bermejo, Inigo Mei, Hailiang Bron, Esther E. |
| author_facet | Mateus, Pedro Garst, Swier Yu, Jing Cats, Davy Harms, Alexander G. J. Birhanu, Mahlet Beekman, Marian Slagboom, P. Eline Reinders, Marcel van der Grond, Jeroen Dekker, Andre Jansen, Jacobus F. A. Beran, Magdalena Schram, Miranda T. Visser, Pieter Jelle Moonen, Justine Ghanbari, Mohsen Roshchupkin, Gennady Vojinovic, Dina Bermejo, Inigo Mei, Hailiang Bron, Esther E. |
| contents | Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to estimate BrainAge in three cohorts. The federated BrainAge model yielded significantly lower error for age prediction across the cohorts than locally trained models. Harmonizing the age interval between cohorts further improved BrainAge accuracy. Subsequently, we compared BrainAge with MetaboAge using federated association and survival analyses. The results showed a small association between BrainAge and MetaboAge as well as a higher predictive value for the time to mortality of both scores combined than for the individual scores. Hence, our study suggests that both aging scores capture different aspects of the aging process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_01235 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning Mateus, Pedro Garst, Swier Yu, Jing Cats, Davy Harms, Alexander G. J. Birhanu, Mahlet Beekman, Marian Slagboom, P. Eline Reinders, Marcel van der Grond, Jeroen Dekker, Andre Jansen, Jacobus F. A. Beran, Magdalena Schram, Miranda T. Visser, Pieter Jelle Moonen, Justine Ghanbari, Mohsen Roshchupkin, Gennady Vojinovic, Dina Bermejo, Inigo Mei, Hailiang Bron, Esther E. Quantitative Methods Machine Learning I.2.1 Biological age scores are an emerging tool to characterize aging by estimating chronological age based on physiological biomarkers. Various scores have shown associations with aging-related outcomes. This study assessed the relation between an age score based on brain MRI images (BrainAge) and an age score based on metabolomic biomarkers (MetaboAge). We trained a federated deep learning model to estimate BrainAge in three cohorts. The federated BrainAge model yielded significantly lower error for age prediction across the cohorts than locally trained models. Harmonizing the age interval between cohorts further improved BrainAge accuracy. Subsequently, we compared BrainAge with MetaboAge using federated association and survival analyses. The results showed a small association between BrainAge and MetaboAge as well as a higher predictive value for the time to mortality of both scores combined than for the individual scores. Hence, our study suggests that both aging scores capture different aspects of the aging process. |
| title | MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning |
| topic | Quantitative Methods Machine Learning I.2.1 |
| url | https://arxiv.org/abs/2409.01235 |