Unraveling the SIGNature of ALzheimer's Disease: Integrating Multimodal Biomarkers Through Machine Learning
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 250
- 试验地点
- 1
- 主要终点
- Diagnostic accuracy of biomarkers in detecting Alzheimer's disease
研究概览
简要总结
This study focuses on improving early detection of Alzheimer's disease (AD) in patients with subjective cognitive decline (SCD), a preclinical stage of cognitive impairment, in the context of emerging disease-modifying therapies (DMTs). Current biomarkers, such as brain MRI, PET scans, and cerebrospinal fluid (CSF) markers, are highly accurate but costly, invasive, and not widely accessible.
The study aims to provide cost-effective, scalable tools for early identification of individuals at risk, enabling personalized assessment and timely DMT administration.
Objectives:
- Evaluate the accuracy of innovative, easily accessible biomarkers in predicting biologically confirmed AD.
- Assess the predictive utility of previously studied methods for SCD patients.
- Explore new approaches, including automated speech analysis, to identify cognitive decline.
- Evaluate genetic contributions to AD risk.
- Integrate data from these various modalities using machine learning to create a predictive model for AD in SCD patients.
Study Design:
This is a multicenter, longitudinal, low-intervention study conducted at IRCCS Policlinico San Donato, San Donato Milanese, Milan, Italy (UO1) and the Center for Research and Innovation in Dementia, Careggi Hospital, Florence, Italy (UO2). Eligible participants are adults with SCD, intact daily functioning, and Mini-Mental State Examination (MMSE) scores >24. Exclusion criteria include neurological or systemic diseases, major psychiatric disorders, substance use, or prior head injury.
Participants undergo:
- Detailed medical and family history collection.
- Comprehensive neuropsychological, personality, and independence in daily activities assessment
- EEG recording in resting state.
- Blood sampling for plasma biomarkers (Aβ42, Aβ40, p-tau181, p-tau217, t-tau, NfL, GFAP).
- CSF biomarker analysis (Aβ42, Aβ40, p-tau, t-tau).
- Genetic analysis of AD-related genes (PSEN1, PSEN2, APOE, TREM2, ABCA7, BDNF, HTT).
- Speech recording and analysis using standardized tasks to extract features for automated evaluation.
The study expects to create a machine learning-based predictive model combining biomarker, neuropsychological, EEG, speech, and genetic data to improve early detection and guide personalized patient care.
Procedures:
- Neuropsychological evaluations occur at baseline and two-year follow-up.
- Language recordings are conducted in controlled settings using standardized picture description tasks.
- EEG is recorded using 21-channel systems.
- Blood and CSF samples are collected, processed, and stored at -80°C for subsequent analysis at respective institutional laboratories.
- Plasma biomarkers are analyzed with Simoa technology; CSF biomarkers are analyzed using chemiluminescent enzyme immunoassay (CLEIA).
- Genetic analyses employ PCR, high-resolution melting analysis (HRMA), sequencing, and capillary electrophoresis as appropriate for specific genes or polymorphisms.
The study expects to create a machine learning-based predictive model combining biomarker, neuropsychological, EEG, speech, and genetic data to improve early detection and guide personalized patient care.
详细描述
- INTRODUCTION Alzheimer's disease (AD) research and clinical practice are at a turning point. As disease-modifying therapies (DMTs) for AD are becoming available, neurologists, researchers, and health services will face a predictable, increasing demand for diagnostic evaluations for patients with cognitive impairment. In addition, there is a consensus that DMTs should be administered in the earliest stages of the disease to halt the disease process before neurodegeneration begins. For this reason, international research is focusing on subjective cognitive decline (SCD) and mild cognitive impairment (MCI), considered the earliest manifestations of AD and the optimal target population for future DMTs. However, both MCI and SCD are very common and heterogeneous conditions with different possible trajectories and many potential underlying causes. Current recognized biomarkers for the disease (brain MRI, PET neuroimaging, and cerebrospinal fluid [CSF] biomarkers) are highly accurate in identifying patients with SCD and MCI due to Alzheimer's disease, but their large-scale use is extremely limited due to high cost, poor accessibility, and invasiveness.
For this reason, previous studies have suggested considering demographic, cognitive, and genetic characteristics to estimate the risk of dementia. In addition, blood-based biomarkers are considered promising tools to enable assessment at the primary care level. However, none of these assessments or tools alone can guarantee sufficient accuracy to be used at the screening level. 2. OBJECTIVE OF THE STUDY
We aim to:
- Evaluate the accuracy of more innovative and easily accessible biomarkers in the earliest stages of cognitive decline in predicting the presence of biologically diagnosed AD based on CSF biomarkers.
- To clarify the utility of techniques that have already been studied in this context but have produced conflicting results, such as neuropsychological scores and electroencephalography (EEG).
- To explore new analysis techniques not yet applied to this field, such as automated analysis of speech recording (speech analysis).
- To evaluate the contribution of genetic variants to AD risk in patients with SCD.
- Combine the features and data extracted from these techniques using a machine learning approach to develop a predictive model of AD in patients with SCD.
3. STUDY DESIGN This is a multicenter, longitudinal, low-intervention study. Patients will be recruited from the U.O.C. of Neurology at IRCCS Policlinico San Donato (henceforth referred to as UO1) and from the Center for Research and Innovation in Dementia (CRIDEM) at Azienda Ospedaliero-Universitaria Careggi in Florence, Italy (AOUC, henceforth referred to as UO2).
研究设计
- 研究类型
- Observational
- 观察模型
- Cohort
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Clinical diagnosis of SCD according to the SCD-I criteria;
- •Mini-Mental State Examination (MMSE) score greater than 24, adjusted for age and education level;
- •Normal functioning on the Activities of Daily Living (ADL) and Instrumental Activities of Daily Living (IADL) scales.
排除标准
- •History of head trauma;
- •Current neurological and/or systemic diseases;
- •Symptoms of psychosis, major depression, or substance use disorder.
研究组 & 干预措施
Subjective Cognitive Decline
Individuals complaining of cognitive decline that are not confirmed by neuropsychological examination
结局指标
主要结局
Diagnostic accuracy of biomarkers in detecting Alzheimer's disease
时间窗: 12-24 months
The accuracy of blood-based biomarkers will be evaluated for predicting a biological diagnosis of AD and for predicting progression of cognitive decline during follow-up. The biological diagnosis of AD will be defined by cerebrospinal fluid biomarker positivity, specifically an abnormal Aβ42/Aβ40 ratio and elevated CSF p-tau181. Progression of cognitive decline will be defined as worsening in at least one cognitive domain, loss of autonomy, or progression to MCI or dementia.
次要结局
- Accuracy of neuropsychological and neurophysiological measures in predicting AD pathology defined according to CSF biomarker profile.(12-24 months)
- Accuracy of automated speech analysis in predicting AD(12-24 months)
- Effect of genetic variants on the risk of AD in patients with SCD(12-24 months)
- A machine learning model to predict AD(12-24 months)
研究者
Salvatore Mazzeo
Assistant Professor in Neurology and Consultant Neurologist
IRCCS Policlinico S. Donato