Univ. Bordeaux, Bordeaux Population Health, UMR1219, Inserm, Bordeaux, France.
CIC 1401 de Bordeaux - Module Epidémiologique Clinique / Bâtiment ISPED, Université de Bordeaux, 146, rue Léo Saignat, Bordeaux cedex, CS61292 33076, France.
Alzheimers Res Ther. 2024 Oct 11;16(1):219. doi: 10.1186/s13195-024-01595-5.
The accumulation of amyloid-β (Aβ) peptide in the brain is a hallmark of Alzheimer's disease (AD), occurring years before symptom onset. Current methods for quantifying in vivo amyloid load involve invasive or costly procedures, limiting accessibility. Early detection of amyloid positivity in non-demented individuals is crucial for aiding early AD diagnosis and for initiating anti-amyloid immunotherapies at early stages. This study aimed to develop and validate predictive models to identify brain amyloid positivity in non-demented patients, using routinely collected clinical data.
Predictive models for amyloid positivity were developed using data from 853 non-demented participants in the MEMENTO cohort. Amyloid levels were measured potentially repeatedly during study course through Positron Emision Tomography or CerebroSpinal Fluid analysis. The probability of amyloid positivity was modelled using mixed-effects logistic regression. Predictors included demographic information, cognitive assessments, visual brain MRI evaluations of hippocampal atrophy and lobar microbleeds, AD-related blood biomarkers (Aβ42/40 and P-tau181), and ApoE4 status. Models were subjected to internal cross-validation and external validation using data from the Amsterdam Dementia Cohort. Performance also was evaluated in a subsample that met the main criteria of the Appropriate Use Recommendations (AUR) for lecanemab.
The most effective model incorporated demographic data, cognitive assessments, ApoE status, and AD-related blood biomarkers, achieving AUCs of 0.82 [95%CI 0.81-0.82] in MEMENTO sample and 0.90 [95%CI 0.86-0.94] in the external validation sample. This model significantly outperformed a reference model based solely on demographic and cognitive data, with an AUC difference in MEMENTO of 0.10 [95%CI 0.10-0.11]. A similar model without ApoE genotype achieved comparable discriminatory performance. MRI markers did not improve model performance. Performances in AUR of lecanemab subsample were comparable.
A predictive model integrating demographic, cognitive, and blood biomarker data offers a promising method to help identify amyloid status in non-demented patients. ApoE genotype and brain MRI data were not necessary for strong discriminatory ability, suggesting that ApoE genotyping may be deferred when assessing the risk-benefit ratio of immunotherapies in amyloid-positive patients who desire treatment. The integration of this model into clinical practice could reduce the need for lumbar puncture or PET examinations to confirm amyloid status.
β淀粉样蛋白（Aβ）在脑内的积累是阿尔茨海默病（AD）的一个标志，在症状出现前多年就已经发生。目前用于量化体内淀粉样蛋白负荷的方法涉及侵入性或昂贵的程序，限制了其可及性。在非痴呆个体中早期发现淀粉样蛋白阳性对于辅助早期 AD 诊断以及在早期阶段启动抗淀粉样蛋白免疫疗法至关重要。本研究旨在开发和验证预测模型，以使用常规收集的临床数据识别非痴呆患者的脑淀粉样蛋白阳性。
使用 MEMENTO 队列中的 853 名非痴呆参与者的数据开发了淀粉样蛋白阳性的预测模型。在研究过程中，通过正电子发射断层扫描或脑脊液分析，可能会重复测量淀粉样蛋白水平。使用混合效应逻辑回归对淀粉样蛋白阳性的概率进行建模。预测因子包括人口统计学信息、认知评估、海马萎缩和脑叶微出血的视觉脑 MRI 评估、AD 相关的血液生物标志物（Aβ42/40 和 P-tau181）以及 ApoE4 状态。使用阿姆斯特丹痴呆队列的数据对模型进行内部交叉验证和外部验证。还在满足 lecanemab 合理使用建议（AUR）主要标准的亚组中评估了性能。
最有效的模型纳入了人口统计学数据、认知评估、ApoE 状态和 AD 相关的血液生物标志物，在 MEMENTO 样本中的 AUC 为 0.82[95%CI 0.81-0.82]，在外部验证样本中的 AUC 为 0.90[95%CI 0.86-0.94]。该模型显著优于仅基于人口统计学和认知数据的参考模型，在 MEMENTO 中 AUC 差异为 0.10[95%CI 0.10-0.11]。没有 ApoE 基因型的类似模型也实现了类似的区分性能。MRI 标志物并未提高模型性能。在 lecanemab 亚组的 AUR 中，性能相当。
整合人口统计学、认知和血液生物标志物数据的预测模型为帮助识别非痴呆患者的淀粉样蛋白状态提供了一种有前途的方法。ApoE 基因型和脑 MRI 数据对于强区分能力不是必需的，这表明在希望接受治疗的淀粉样蛋白阳性患者中评估免疫疗法的风险-获益比时，可以推迟 ApoE 基因分型。将该模型纳入临床实践可能会减少腰椎穿刺或 PET 检查以确认淀粉样蛋白状态的需要。
