While biological age scores have been shown to characterize aging by estimating chronological age based on physiological biomarkers, interactions between different age scores are largely unknown. To study this, large-scale multi-modal data are crucial. However, such data are scarce as population-based cohorts are generally restricted in sharing their data. Here, we employ federated learning to study the relationship between the two types of biological age scores: BrainAge based on brain MRI and MetaboAge based on metabolites. Using three large population-based cohorts, we trained a federated deep learning model to estimate BrainAge and compared its performance to models trained in a single cohort. 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 and MetaboAge by performing association analysis and survival analysis for dementia and mortality prediction to further characterize both scores. The association analysis showed a weak association between BrainAge and MetaboAge, while the survival analysis indicated complementary predictive values for the mortality risk of the two scores. Federated learning has been shown to be a valuable technique for enabling the use of research cohorts that are restricted in data sharing. We conclude that BrainAge and MetaboAge act synergetically for the prediction of time to all-cause mortality, and both aging scores capture different aspects of the aging process.

Background
Aggregation of cohort data increases precision for studying neurodegenerative disease pathways, but efforts to combine data and expertise are often hampered by infrastructural, ethical and legal considerations. We aimed to unite various cohort studies in the Netherlands to enhance research infrastructure and facilitate research on dementia etiology and its public health implications.

Methods
The Netherlands Consortium of Dementia Cohorts (NCDC) includes participants with initially no established cognitive impairment from 9 Dutch cohorts: the Amsterdam Dementia Cohort (ADC), Doetinchem Cohort Study (DCS), European Medical Information Framework for Alzheimer’s Disease (EMIF-AD), Longitudinal Aging Study Amsterdam (LASA), the Leiden Longevity Study (LLS), The Maastricht Study, the Memolife substudy of the Lifelines cohort, Rotterdam Study and Second Manifestations of ARTerial disease-Magnetic Resonance (SMART-MR) study. The objectives of NCDC are to improve data infrastructure and access to cohorts related to aging and dementia, investigate the role of Alzheimer’s disease and vascular pathology in the development of dementia and estimate the public health impact of established dementia risk factors by assessing their relative contribution to the population burden of dementia.

Results
We increased the findability, accessibility, interoperability and reusability (FAIR) status of the cohorts through harmonization of data across cohorts, implementation of medical imaging repositories for scan management, implementation of the Personal Health Train infrastructure and provision of meta-data in existing cohort catalogues. We established the ethical and legal frameworks required for federated and pooled analyses and performed the first remote federated data analyses using the Personal Health Train infrastructure. To determine biomarkers of Alzheimer’s disease, endothelial dysfunction and inflammation, 2554 plasma samples were analyzed centrally. Federated, pooled, and coordinated meta-analyses have led to multiple publications in the context of NCDC.

Conclusion
The combination of population-based and clinical cohorts, the coordinated assessment of plasma markers in previously collected samples and implementation and use of the Personal Health Train infrastructure for federated analysis are both feasible and promising for future collaborative efforts.