Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults
<b>Background:</b> 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-symp...
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2025-05-01
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| author | Oriehi Anyaiwe Nandini Nataraj Bhargava Sai Gudikandula |
| author_facet | Oriehi Anyaiwe Nandini Nataraj Bhargava Sai Gudikandula |
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| description | <b>Background:</b> 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. <b>Methods:</b> 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 (A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>β</mi><mn>42</mn></msub></semantics></math></inline-formula>), 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 A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>β</mi><mn>42</mn></msub></semantics></math></inline-formula> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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. |
| format | Article |
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| institution | Kabale University |
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| spelling | doaj-art-efd5b3c2ba9c4b9ab83ff396adbfc25b2025-08-20T03:46:50ZengMDPI AGDiagnostics2075-44182025-05-011511132710.3390/diagnostics15111327Computational Risk Stratification of Preclinical Alzheimer’s in Younger AdultsOriehi Anyaiwe0Nandini Nataraj1Bhargava Sai Gudikandula2Department of Mathematics and Computer Science, College of Arts and Sciences, Lawrence Technological University, Southfield, MI 48075, USADepartment of Mathematics and Computer Science, College of Arts and Sciences, Lawrence Technological University, Southfield, MI 48075, USADepartment of Mathematics and Computer Science, College of Arts and Sciences, Lawrence Technological University, Southfield, MI 48075, USA<b>Background:</b> 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. <b>Methods:</b> 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 (A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>β</mi><mn>42</mn></msub></semantics></math></inline-formula>), 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 A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mi>β</mi><mn>42</mn></msub></semantics></math></inline-formula> 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. <b>Results:</b> 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. <b>Conclusions:</b> 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.https://www.mdpi.com/2075-4418/15/11/1327Alzheimer’s diseasecognitive impairmentbiomarkersmachine learningcomputational modelingage-specific risk |
| spellingShingle | Oriehi Anyaiwe Nandini Nataraj Bhargava Sai Gudikandula Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults Diagnostics Alzheimer’s disease cognitive impairment biomarkers machine learning computational modeling age-specific risk |
| title | Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults |
| title_full | Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults |
| title_fullStr | Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults |
| title_full_unstemmed | Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults |
| title_short | Computational Risk Stratification of Preclinical Alzheimer’s in Younger Adults |
| title_sort | computational risk stratification of preclinical alzheimer s in younger adults |
| topic | Alzheimer’s disease cognitive impairment biomarkers machine learning computational modeling age-specific risk |
| url | https://www.mdpi.com/2075-4418/15/11/1327 |
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