Anyaiwe Oriehi, Nataraj Nandini, Gudikandula Bhargava Sai
Department of Mathematics and Computer Science, College of Arts and Sciences, Lawrence Technological University, Southfield, MI 48075, USA.
Diagnostics (Basel). 2025 May 26;15(11):1327. doi: 10.3390/diagnostics15111327.
Alzheimer's disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection remains critical for effective intervention, particularly in younger adults, where biomarker deviations may signal pre-symptomatic risk. This research presents a computational modeling framework to predict cognitive impairment progression and stratify individuals into risk zones based on age-specific biomarker thresholds. The model integrates sigmoid-based data generation to simulate non-linear biomarker trajectories reflective of real-world disease progression. Core biomarkers-including cerebrospinal fluid (CSF) amyloid-beta 42 (Aβ42), amyloid positron emission tomography (amyloid PET), cerebrospinal fluid Tau protein (CSF Tau), and magnetic resonance imaging with fluorodeoxyglucose positron emission tomography (MRI FDG-PET)-were analyzed simultaneously to compute the cognitive impairment (CI) score of instances, dynamically adjusted for age. Higher CSF Aβ42 levels consistently demonstrated a protective effect, while elevated amyloid PET and Tau levels increased cognitive risk. Age-specific CI thresholds prevented the overestimation of risk in younger individuals and the underestimation in older cohorts. To demonstrate its applicability, we applied the full four-stage framework-comprising data aggregation and cleaning, sigmoid-based synthetic biomarker simulation with descriptive analysis, parameter accumulation modeling, and correlation-driven CI classification-on a curated dataset of 307 instances (ages 10-110) from Kaggle, the Alzheimer's Disease Neuroimaging Initiative (ANDI), and the Open Access Series of Imaging Studies (OASIS) to evaluate age-specific stratification of preclinical AD risk. The study highlights the model's potential to identify individuals in risk zones from a pool of 150 instances, enabling targeted early interventions. Furthermore, the framework supports retrospective disease trajectory analysis, offering clinicians insights into optimal intervention windows even after symptom onset. Future work aims to validate the model using longitudinal, inclusive, real-world datasets and expand its predictive capacity through machine learning techniques and integrating genetic and lifestyle factors. Ultimately, this research contributes to advancing precision medicine approaches in Alzheimer's disease by providing a scalable computational tool for early risk assessment and intervention planning.
阿尔茨海默病（AD）是一种进行性神经退行性疾病，通常在临床症状出现前数十年就已开始。早期检测对于有效干预仍然至关重要，尤其是在年轻人中，生物标志物偏差可能预示着症状前风险。本研究提出了一个计算建模框架，用于预测认知障碍进展，并根据特定年龄的生物标志物阈值将个体分层到风险区域。该模型集成了基于 sigmoid 的数据生成，以模拟反映现实世界疾病进展的非线性生物标志物轨迹。同时分析了核心生物标志物，包括脑脊液（CSF）淀粉样蛋白β42（Aβ42）、淀粉样正电子发射断层扫描（淀粉样 PET）、脑脊液 Tau 蛋白（CSF Tau）以及氟脱氧葡萄糖正电子发射断层扫描磁共振成像（MRI FDG-PET），以计算实例的认知障碍（CI）得分，并根据年龄进行动态调整。较高的 CSF Aβ42 水平始终显示出保护作用，而淀粉样 PET 和 Tau 水平升高则增加认知风险。特定年龄的 CI 阈值可防止高估年轻人的风险和低估老年人群的风险。为了证明其适用性，我们在来自 Kaggle、阿尔茨海默病神经影像倡议（ANDI）和开放获取影像研究系列（OASIS）的 307 个实例（年龄 10 - 110 岁）的精选数据集上应用了完整的四阶段框架，包括数据聚合与清理、基于 sigmoid 的合成生物标志物模拟及描述性分析、参数累积建模以及相关性驱动的 CI 分类，以评估临床前 AD 风险的特定年龄分层。该研究强调了该模型从 150 个实例中识别处于风险区域个体的潜力，从而实现有针对性的早期干预。此外，该框架支持回顾性疾病轨迹分析，即使在症状出现后也能为临床医生提供最佳干预窗口的见解。未来的工作旨在使用纵向、全面的真实世界数据集验证该模型，并通过机器学习技术以及整合遗传和生活方式因素来扩展其预测能力。最终，本研究通过提供一种可扩展的计算工具用于早期风险评估和干预规划，为推进阿尔茨海默病的精准医学方法做出了贡献。
