MRI与代谢组学年龄评分联合预测死亡风险更准。
MRI-based and metabolomics-based age scores act synergetically for mortality prediction shown by multi-cohort federated learning
- 用联邦学习融合多队列MRI和代谢数据，提升年龄预测精度。
- 两者联合预测死亡时间的效能优于单独使用任一指标。
- 适合关注衰老机制与生物标志物融合研究的学者。
生物年龄评分是通过生理生物标志物估算时序年龄的新工具，与衰老相关结局有关联。本研究评估了基于脑部MRI图像的年龄评分（BrainAge）与基于代谢组生物标志物的年龄评分（MetaboAge）之间的关系。我们在三个队列中训练了联邦深度学习模型来估计BrainAge，结果表明，联邦模型在各队列中的年龄预测误差显著低于本地训练模型；进一步对队列间年龄区间进行标准化后，BrainAge的准确性得到提升。随后，我们采用联邦关联分析与生存分析比较BrainAge与MetaboAge。结果显示，两者间存在较弱的相关性，但联合使用时对死亡时间的预测能力高于单一评分。因此，本研究提示，两种年龄评分反映了衰老过程的不同方面。
原文摘要 · Abstract (English)
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.
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