Finding signs of Alzheimer's in people with subjective memory complaints
Official title: Unraveling the SIGNature of ALzheimer's Disease: Integrating Multimodal Biomarkers Through Machine Learning
Run by IRCCS Policlinico S. Donato · NCT07402161 · Data updated 2026-11-12
The study will test whether inexpensive, easy-to-get measures—like blood markers, EEG and automated speech analysis—can help identify early Alzheimer's in people who notice memory or thinking changes but have normal standard tests.
Quick facts
| Study type | Observational |
|---|---|
| Ages | 18 Years and up |
| Sex | All |
| Enrollment | 250 (estimated) |
| Sponsor | IRCCS Policlinico S. Donato Academic / other |
| Locations | 1 site (San Donato Milanese, Milan) |
| Status | Recruiting |
| Started | October 1, 2025 |
| Main results expected | October 1, 2027 |
| Study ends | March 1, 2028 |
| Registry record updated | February 11, 2026 |
| Trial ID | NCT07402161 on ClinicalTrials.gov |
Is this trial a fit for you?
✓Likely a good fit
Ideal candidates are adults with subjective cognitive decline per SCD‑I criteria who have MMSE scores >24, normal ADL/IADL function, and no major neurological or psychiatric disorders.
✕Probably not a fit
Patients who already have objective cognitive impairment (MCI or dementia), active neurological or systemic disease, major depression, psychosis, substance use disorders, or a history of significant head trauma are unlikely to benefit from this protocol.
A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.
What this trial studies
This observational study enrolls people with subjective cognitive decline and collects multimodal data including blood-based biomarkers, EEG, genetic testing, automated speech samples, and clinical/neuropsychological measures, with MRI/PET/CSF used when available as biological reference standards. Participants meet SCD‑I criteria, have MMSE scores above 24, and normal daily functioning, and are followed cross-sectionally to link noninvasive measures to underlying Alzheimer's biology. Machine learning models will integrate these diverse data to generate risk signatures that predict biologically confirmed AD. The goal is to create scalable, low-cost tools to triage patients for further testing or early treatment.
Why it matters
Potential benefit: If successful, this approach could provide low-cost, noninvasive ways to identify people at high risk for Alzheimer's earlier and help them access disease-modifying treatments sooner.
How similar studies have gone: Previous studies of blood biomarkers, EEG, automated speech analysis, and machine-learning risk models have shown promising but preliminary results that need larger, integrated validation.
Who can join: the full rules
Show full inclusion / exclusion criteria
Inclusion Criteria: * 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. Exclusion Criteria: * History of head trauma; * Current neurological and/or systemic diseases; * Symptoms of psychosis, major depression, or substance use disorder.
Where it's running
-
IRCCS Policlinico San Donato
San Donato Milanese, Milan, Italy Recruiting
Study contacts
- Principal investigator: Salvatore Mazzeo, MD, PhD — Università Vita-Salute San Raffaele, Milano - Neurology Unit, IRCCS Policlinico San Donato, San Donato Milanese
- Study coordinator: Mattia Ricotti
- Email: mattia.ricotti@grupposandonato.it
- Phone: +390252774236
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How to take part
- Review the eligibility criteria above with your treating physician.
- Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
- Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.