AI 中文总结
该研究开发多模态框架NetMoint，整合多组学与影像数据预测不同痴呆亚型的个体化长期风险，识别出小比例高风险群体及其特异性分子特征，为痴呆风险分层提供新方法。
AI 中文摘要
痴呆是一类具有生物学异质性的疾病，但当前的风险评估对于临床诊断前特定亚型的风险如何产生和分化提供的见解有限。我们开发了NetMoint这一多模态框架，整合部分观测的血浆蛋白质组学、结构磁共振成像和脑血流动力学表型，用于预测1年、5年、10年和20年时间范围内阿尔茨海默病（AD）、血管性痴呆（VD）和额颞叶痴呆（FTD）的个体化风险。在基线时无痴呆的104120名英国生物银行（UK Biobank）参与者中，NetMoint对AD、VD和FTD的受试者工作特征曲线下面积（AUC）均值分别达到0.937、0.930和0.932。预测的生物学决定因素随时间变化，从较短时间范围内的脑结构脆弱性转向较长时间范围内的循环分子特征，且具有不同的亚型特异性生物学特征。多时间范围风险分析识别出痴呆易感性的不同时间轨迹：在后续发展为AD的参与者中，0.7%遵循持续的极高风险轨迹，20年时预测风险达53.50%；而在发展为FTD的参与者中，8.3%遵循上升的极高风险轨迹，20年时达67.17%。这些高风险轨迹具有不同的分子特征，AD组以较低的TGFB1为特征，FTD组以较高的NDRG1为特征。在ADNI到UK Biobank的独立分析中，经138个共享特征 harmonization（ harmonization 保留原术语）后，AD风险预测仍具有信息价值，20年时AUC为0.741。综上，这些发现建立了一个可解析轨迹的多模态痴呆风险分层框架，识别出痴呆亚型中规模较小但风险较高的群体，并将其不同的风险轨迹与独特的分子特征关联起来。
英文摘要
Dementia comprises biologically heterogeneous disorders, yet current risk assessment provides limited insight into how subtype-specific risk emerges and diverges before clinical diagnosis. We developed NetMoint, a multimodal framework integrating partially observed plasma proteomic, structural magnetic resonance imaging and cerebral haemodynamic phenotypes to predict individualized risks of Alzheimer's disease (AD), vascular dementia (VD) and frontotemporal dementia (FTD) across 1-, 5-, 10- and 20-year horizons. Among 104,120 UK Biobank participants free of dementia at baseline, NetMoint achieved mean area under the receiver operating characteristic curve (AUC) values of 0.937, 0.930 and 0.932 for AD, VD and FTD, respectively. The biological determinants of prediction shifted with time, from structural brain vulnerability at shorter horizons towards circulating molecular signatures at longer horizons, with distinct subtype-specific biological profiles. Multi-horizon risk profiling identified distinct temporal trajectories of dementia susceptibility. Among participants who subsequently developed AD, 0.7% followed a persistently very-high-risk trajectory, with predicted risk reaching 53.50% at 20 years, whereas 8.3% of those who developed FTD followed an increasing very-high-risk trajectory, reaching 67.17%. These high-risk trajectories were marked by distinct molecular signatures, with lower TGFB1 characterizing the AD group and higher NDRG1 the FTD group. In an independent ADNI-to-UK Biobank analysis, AD risk prediction remained informative after harmonization to 138 shared features, with an AUC of 0.741 at 20 years. Together, these findings establish a multimodal framework for trajectory-resolved dementia risk stratification, identifying small but high-risk populations within dementia subtypes and linking their divergent risk trajectories to distinct molecular signatures.
