Weber Darren M, Stroh Matthew A, Taylor Steven W, Lagier Robert J, Louie Judy Z, Clarke Nigel J, Vaillancourt David E, Rayaprolu Sruti, Duara Ranjan, Racke Michael K
Quest Diagnostics Nichols Institute, San Juan Capistrano, CA.
Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA.
medRxiv. 2025 Apr 24:2025.02.27.25322892. doi: 10.1101/2025.02.27.25322892.
Plasma biomarkers provide new tools to evaluate patients with mild cognitive impairment (MCI) for Alzheimer's disease (AD) pathology. Such tools are needed for anti-amyloid therapies that require efficient and accurate diagnostic evaluation to identify potential treatment candidates. This study sought to develop and evaluate the clinical performance of a multi-marker combination of plasma beta-amyloid 42/40 (Aβ42/40), ptau-217, and genotype to predict amyloid PET positivity in a diverse cohort of patients at a memory clinic and evaluate >4,000 results from "real-world" specimens submitted for high-throughput clinical testing.
Study participants were from the 1Florida AD Research Center (ADRC). Demographics, clinical evaluations, and amyloid PET scan data were provided with plasma specimens for model development for the intended-use cohort (MCI/AD: n=215). Aβ42/40 and ApoE4 proteotype (reflecting high-risk 4 alleles) were measured by mass spectrometry and ptau-217 by immunoassay. A likelihood score model was determined for each biomarker separately and in combination. Model performance was optimized using 2 cutpoints, 1 for high and 1 for low likelihood of PET positivity, to attain ≥90% specificity and sensitivity. These cutpoints were applied to categorize 4,326 real-world specimens and an expanded cohort stratified by cognitive status (normal cognition [NC], MCI, AD).
For the intended-use cohort (46.0% prevalence of PET-positivity), a combination of Aβ42/40, ptau-217, and allele count provided the best model with a receiver operating characteristic area under the curve (ROC-AUC) of 0.942 and with 2 cutpoints fixed at 91% sensitivity and 91% specificity yielding a high cutpoint with 88% positive predictive value (PPV) and 87% accuracy and a low cutpoint with 91% negative predictive value (NPV) and 85% accuracy. Incorporating allele count also reduced the percentage of patients with indeterminate risk from 15% to 10%. The cutpoints categorized the real-world clinical specimens as having 42% high, 51% low, and 7% indeterminate likelihood for PET positivity and differentiated between NC, MCI, and AD dementia cognitive status in the expanded cohort.
Combining plasma biomarkers Aβ42/40, ptau-217, and allele count is a scalable approach for evaluating patients with MCI for suspected AD pathology.
血浆生物标志物为评估轻度认知障碍（MCI）患者的阿尔茨海默病（AD）病理提供了新工具。对于需要高效准确诊断评估以识别潜在治疗候选者的抗淀粉样蛋白疗法而言，此类工具不可或缺。本研究旨在开发并评估血浆β-淀粉样蛋白42/40（Aβ42/40）、磷酸化tau蛋白217（ptau-217）和基因型的多标志物组合在记忆门诊不同患者队列中预测淀粉样蛋白PET阳性的临床性能，并评估提交用于高通量临床检测的“真实世界”样本的4000多个结果。
研究参与者来自佛罗里达AD研究中心（ADRC）。人口统计学、临床评估和淀粉样蛋白PET扫描数据与血浆样本一同提供，用于目标使用队列（MCI/AD：n = 215）的模型开发。通过质谱法测量Aβ42/40和载脂蛋白E4蛋白型（反映高风险4个等位基因），通过免疫测定法测量ptau-217。分别单独及联合确定每个生物标志物的似然评分模型。使用两个切点优化模型性能，一个用于PET阳性高似然，一个用于PET阳性低似然，以达到≥90%的特异性和敏感性。将这些切点应用于对4326个真实世界样本以及按认知状态（正常认知[NC]、MCI、AD）分层的扩大队列进行分类。
对于目标使用队列（PET阳性患病率为46.0%），Aβ42/40、ptau-217和等位基因计数的组合提供了最佳模型，曲线下受试者工作特征面积（ROC-AUC）为0.942，两个切点固定在91%的敏感性和91%的特异性，高切点的阳性预测值（PPV）为88%，准确率为87%，低切点的阴性预测值（NPV）为91%，准确率为85%。纳入等位基因计数还将风险不确定患者的百分比从15%降至10%。这些切点将真实世界临床样本分类为PET阳性高似然的占42%、低似然的占51%、不确定似然的占7%，并在扩大队列中区分了NC、MCI和AD痴呆的认知状态。
联合血浆生物标志物Aβ42/40、ptau-217和等位基因计数是一种可扩展的方法，用于评估疑似AD病理的MCI患者。
