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.
medRxiv. 2024 Nov 15:2024.11.13.24317270. doi: 10.1101/2024.11.13.24317270.
A growing body of evidence suggests that neuroinflammation contributes actively to pathophysiology of Alzheimer's disease (AD) and promotes AD progression. The predictive value of neuroinflammatory biomarkers for disease-staging or estimating disease progression is not well understood. In this study, we investigate the diagnostic and prognostic utility of inflammatory biomarkers in combination with conventional AD biomarkers.
We included 258 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had CSF biomarkers of β-Amyloid (Aβ), tau, and inflammation. The primary outcome of interest was clinically meaningful cognitive decline (CMCD) as defined by an increase of ≥4 on the Alzheimer's Disease Assessment Scale Cognitive Subscore 11 (ADAS-11, scores 0-70, higher scores indicate worse cognition). Predictor variables were categorized as demographics (D; age, sex, and education), genetic (APOE4 status (A)), inflammatory biomarkers (I), and classic (C) cerebrospinal fluid (CSF) biomarkers of Aβ and p-tau181. Simultaneous inclusion of eleven CSF inflammatory biomarkers as covariates in logistic regression models was examined to assess improvements in classifying baseline clinical diagnoses (cognitively normal (CN), mild cognitive impairment (MCI), Dementia) and predicting individuals with and without CMCD over one year of follow-up.
At 1-year follow up, 27.1% of participants experienced CMCD. Inclusion of inflammatory biomarkers improved baseline classification of CN vs MCI as well as CN vs Dementia for models including D and A variables (DA; both <0.001). Similarly, when classic CSF biomarkers of AD were included into the model (DAC model), inclusion of inflammatory markers improved classification of CN vs MCI (<0.01) as well as CN vs Dementia (<0.001). Addition of inflammatory biomarkers to both DA and DAC models improved predictive performance for CMCD in persons with baseline MCI and Dementia (all <0.05), but not in the CN group. In addition, the predictive performance of the DAI model was superior to the DAC model in the MCI and Dementia groups (both <0.05).
Addition of CSF inflammatory biomarkers to CSF biomarkers of AD can improve diagnostic accuracy of clinical disease stage at baseline and add incremental value to AD biomarkers for prediction of cognitive decline.
越来越多的证据表明，神经炎症在阿尔茨海默病（AD）的病理生理学中发挥着积极作用，并促进AD的进展。神经炎症生物标志物对疾病分期或估计疾病进展的预测价值尚未得到充分了解。在本研究中，我们调查了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。
我们纳入了来自阿尔茨海默病神经影像学倡议（ADNI）的258名参与者，他们具有β-淀粉样蛋白（Aβ）、tau和炎症的脑脊液生物标志物。感兴趣的主要结局是具有临床意义的认知衰退（CMCD），定义为阿尔茨海默病评估量表认知子量表11（ADAS-11，评分0-70，分数越高表明认知越差）增加≥4分。预测变量分为人口统计学（D；年龄、性别和教育程度）、遗传（APOE4状态（A））、炎症生物标志物（I）以及Aβ和p-tau181的经典（C）脑脊液（CSF）生物标志物。在逻辑回归模型中同时纳入11种脑脊液炎症生物标志物作为协变量，以评估在分类基线临床诊断（认知正常（CN）、轻度认知障碍（MCI）、痴呆）以及预测随访一年中有无CMCD个体方面的改善情况。
在1年的随访中，27.1%的参与者经历了CMCD。对于包括D和A变量（DA）的模型，纳入炎症生物标志物改善了CN与MCI以及CN与痴呆的基线分类（均<0.001）。同样，当将AD的经典脑脊液生物标志物纳入模型（DAC模型）时，纳入炎症标志物改善了CN与MCI的分类（<0.01）以及CN与痴呆的分类（<0.001）。在DA和DAC模型中添加炎症生物标志物均改善了基线MCI和痴呆患者CMCD的预测性能（均<0.05），但在CN组中未改善。此外，在MCI和痴呆组中，DAI模型预测性能优于DAC模型（均<0.05）。
在AD的脑脊液生物标志物中添加脑脊液炎症生物标志物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衰退增加额外价值。
