Investigating brain-enriched proteins with machine learning methods may enable a brain-specific understanding of brain aging and provide insights into the molecular mechanisms and pathological pathways of dementia. The study aims to analyze associations of brain-specific plasma proteomic aging signature with risks of incident dementia. In 45,429 dementia-free UK Biobank participants at baseline, we generated a brain-specific biological age using 63 brain-enriched plasma proteins with machine learning methods. The brain age gap was estimated, and Cox proportional hazards models were used to study the association with incident all-cause dementia, Alzheimer’s disease (AD), and vascular dementia. Per-unit increment in the brain age gap z-score was associated with significantly higher risks of all-cause dementia (hazard ratio [95% confidence interval], 1.67 [1.56–1.79], P < 0.001), AD (1.85 [1.66–2.08], P < 0.001), and vascular dementia (1.86 [1.55–2.24], P < 0.001), respectively. Notably, 2.1% of the study population exhibited extreme old brain aging defined as brain age gap z-score > 2, correlating with over threefold increased risks of all-cause dementia and vascular dementia (3.42 [2.25–5.20], P < 0.001, and 3.41 [1.05–11.13], P = 0.042, respectively), and fourfold increased risk of AD (4.45 [2.32–8.54], P < 0.001). The associations were stronger among participants with healthier lifestyle factors (all P-interaction < 0.05). These findings were corroborated by magnetic resonance imaging assessments showing that a higher brain age gap aligns global pathophysiology of dementia, including global and regional atrophy in gray matter, and white matter lesions (P < 0.001). The brain-specific proteomic age gap is a powerful biomarker of brain aging, indicative of dementia risk and neurodegeneration.

使用机器学习方法研究富含大脑的蛋白质可能有助于对大脑衰老的大脑特异性理解，并提供对痴呆的分子机制和病理途径的见解。该研究旨在分析脑特异性血浆蛋白质组学衰老特征与痴呆风险的关联。在基线时 45,429 名无痴呆症的英国生物样本库参与者中，我们使用 63 种富含大脑的血浆蛋白和机器学习方法生成了大脑特异性生物年龄。估计脑年龄差距，并使用 Cox 比例风险模型研究与发生全因痴呆、阿尔茨海默病 （AD） 和血管性痴呆的相关性。脑年龄差距 z 评分的每单位增量与全因痴呆 （风险比 [95% 置信区间] ，1.67 [1.56–1.79]，P < 0.001）、AD （1.85 [1.66–2.08]，P < 0.001） 和血管性痴呆 （1.86 [1.55–2.24]，P < 0.001） 的风险显著增加相关。值得注意的是，2.1% 的研究人群表现出极度老年脑衰老，定义为脑年龄差距 z 评分 > 2，与全因痴呆和血管性痴呆的风险增加三倍以上相关 （3.42 [2.25–5.20]，P < 0.001 和 3.41 [1.05–11.13]，P = 0.042，分别为 3.042），AD 风险增加四倍 （4.45 [2.32–8.54]，P < 0.001）。在具有更健康生活方式因素的参与者中，相关性更强 （所有 P 交互作用 < 0.05）。磁共振成像评估证实了这些发现，表明较高的脑年龄差距与痴呆的整体病理生理学一致，包括灰质的整体和区域萎缩以及白质病变 （P < 0.001）。 大脑特异性蛋白质组学年龄差距是大脑衰老的有力生物标志物，表明痴呆风险和神经退化。