Ghanbarian Elham, Khorsand Babak, Petersen Kellen K, Nallapu Bhargav T, Sajjadi S Ahmad, Lipton Richard B, Ezzati Ali
Department of Neurology, University of California, Irvine, CA, USA.
Department of Neurology, Washington University in St Louis, St Louis, MO, USA.
J Alzheimers Dis. 2025 Jun;105(4):1298-1308. doi: 10.1177/13872877251335915. Epub 2025 Apr 22.
BackgroundNeuroinflammation actively contributes to the pathophysiology of Alzheimer's disease (AD); however, the value of neuroinflammatory biomarkers for disease-staging or predicting disease progression remains unclear.ObjectiveTo investigate diagnostic and prognostic utility of inflammatory biomarkers in combination with conventional AD biomarkers.MethodsData from 258 participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI) with cerebrospinal fluid (CSF) biomarkers of amyloid-β (Aβ), tau, and inflammation were analyzed. Clinically meaningful cognitive decline (CMCD) was defined as a ≥ 4-point increase on the Alzheimer's Disease Assessment Scale Cognitive Subscore 11. Predictor variables included demographics (D: age, sex, education), status (A), inflammatory biomarkers (I), and classic AD biomarkers of Aβ and p-tau181 (C). Models incorporating inflammatory biomarkers assessed their contribution to improving baseline diagnostic classification and 1-year CMCD prediction.ResultsAt 1-year follow-up, 27.1% of participants experienced CMCD. Adding inflammatory biomarkers to models with D and A variables (DA model) improved classification of cognitively normal (CN) versus mild cognitive impairment (MCI) and CN versus Dementia (< 0.001). Similarly, inflammatory markers enhanced classification in models including C (DAC model), for CN versus MCI (< 0.01) and CN versus Dementia (< 0.001). Predictive performance for CMCD was improved in individuals with MCI and dementia in both models (all < 0.05). In addition, the DAI model outperformed the DAC model in predicting CMCD for MCI and Dementia groups (both < 0.05).ConclusionsAddition of CSF inflammatory biomarkers to biomarkers of AD improves diagnostic accuracy of clinical disease stage at baseline and add incremental value to AD biomarkers for prediction of cognitive decline.
背景
神经炎症在阿尔茨海默病（AD）的病理生理学中起着积极作用；然而，神经炎症生物标志物在疾病分期或预测疾病进展方面的价值仍不明确。
目的
研究炎症生物标志物与传统AD生物标志物联合使用时的诊断和预后效用。
方法
对来自阿尔茨海默病神经影像学倡议（ADNI）的258名参与者的数据进行分析，这些参与者具有脑脊液（CSF）中淀粉样蛋白-β（Aβ）、tau和炎症的生物标志物。具有临床意义的认知下降（CMCD）被定义为阿尔茨海默病评估量表认知子量表11得分增加≥4分。预测变量包括人口统计学特征（D：年龄、性别、教育程度）、状态（A）、炎症生物标志物（I）以及Aβ和p-tau181的经典AD生物标志物（C）。纳入炎症生物标志物的模型评估了它们对改善基线诊断分类和1年CMCD预测的贡献。
结果
在1年的随访中，27.1%的参与者出现了CMCD。将炎症生物标志物添加到包含D和A变量的模型（DA模型）中，改善了认知正常（CN）与轻度认知障碍（MCI）以及CN与痴呆之间的分类（<0.001）。同样，炎症标志物在包含C的模型（DAC模型）中增强了CN与MCI（<0.01）以及CN与痴呆之间的分类（<0.001）。在这两个模型中，MCI和痴呆患者的CMCD预测性能均得到改善（均<0.05）。此外，DAI模型在预测MCI和痴呆组的CMCD方面优于DAC模型（均<0.05）。
结论
将脑脊液炎症生物标志物添加到AD生物标志物中，可提高基线时临床疾病阶段的诊断准确性，并为AD生物标志物预测认知下降增加额外价值。
