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.
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.
History of head trauma;
Current neurological and/or systemic diseases;
Symptoms of psychosis, major depression, or substance use disorder.
Participants will be consecutively recruited among patients referred for cognitive disorders to the Neurology Unit (U.O.C.) of IRCCS Policlinico San Donato (hereinafter referred to as UO1) and to the Research and Innovation Center for Dementia (CRIDEM) of the Careggi University Hospital in Florence (AOUC, hereinafter referred to as UO2).
Cohort
Prospective
Not listed
Diagnostic accuracy of biomarkers in detecting Alzheimer's disease
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.
Accuracy of neuropsychological and neurophysiological measures in predicting AD pathology defined according to CSF biomarker profile.
Classical regression models and machine learning models to predict the AD pathology. AD pathology will be defined according to the CSF biomarker profile as follows: A+ if at least one of the core biomarkers (CSF Aβ42/Aβ40 ratio\<0.069, p-tau\>56,5 ng/L) is positive, and as A- if none of the core biomarkers is positive. The following neuropsychological scores will be considered as predictors: global measurements, Digit and Visuo-spatial Span, Rey Auditory Verbal Learning Test, Short Story Recall, Rey-Osterrieth complex figure copy and recall, Trail Making Test, attentional matrices, Multiple Features Targets Cancellation, Category Fluency Task, Phonemic Fluency Task, the Screening for Aphasia NeuroDegeneration, Copying drawings. Regarding neurophysiological measures, we will use as predictors the following EEG features: absolute and relative power in frequency bands (alpha, beta, theta, delta); Peak frequency and individual alpha frequency; Power spectral density metrics.
Accuracy of automated speech analysis in predicting AD
Classical regression models and machine learning models to predict the AD pathology. AD pathology will be defined according to the CSF biomarker profile as follows: A+ if at least one of the core biomarkers (CSF Aβ42/Aβ40 ratio\<0.069, p-tau\>56,5 ng/L) is positive, and as A- if none of the core biomarkers is positive. Automated speech analysis will be used to extract the following features that will be used as predictors in the model: acoustic and voice quality measures (fundamental frequency, intensity, jitter, shimmer, formant frequencies); prosodic features (speech and articulation rate, pitch variability, pause frequency and duration, intonation patterns); temporal and fluency measures (phonation time, response latency, filled and unfilled pauses, disfluencies); and linguistic features derived from automatic transcripts, including lexical diversity, word frequency, part-of-speech distributions, sintactic complexity, and semantic coherence.
Effect of genetic variants on the risk of AD in patients with SCD
A logistic regression model and a Cox Regression model will be used to estimate the risk of AD. AD pathology will be defined according to the CSF biomarker profile as follows: AD pathology will be defined according to the CSF biomarker profile as follows: A+ if at least one of the core biomarkers (CSF Aβ42/Aβ40 ratio\<0.069, p-tau\>56,5 ng/L) is positive, and as A- if none of the core biomarkers is positive.. Genetic variants in candidate genes (PSEN1, PSEN2, APOE, TREM2, ABCA7, BDNF, HTT and APOE), will be considered as indipendent variables in the model.
A machine learning model to predict AD
Features identified in the previous sections (plasma biomarkers, neuropsychological, neurophysiological, and genetic measures) will be included as predictors in a multimodal machine learning model. A machine learning algorithm will be trained to predict AD pathology defined according to the CSF biomarker profile as follows: A+ if at least one of the core biomarkers (CSF Aβ42/Aβ40 ratio\<0.069, p-tau\>56,5 ng/L) is positive, and as A- if none of the core biomarkers is positive. Thirty percent of the entire dataset will be reserved as a test set, while the remaining 70% will be used for training and validation. A five-fold cross-validation approach will be employed to train the models and optimize hyperparameters. The best-performing model, as evaluated by the area under the ROC curve (AUC), will be tested on the held-out test set to obtain an unbiased estimate of model performance.
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IRCCS Policlinico S. Donato
Lead sponsor
Azienda Ospedaliero-Universitaria Careggi
Collaborator
