Clinical Research Directory

Digital Accessible Remote Olfactory Mediated Health Assessments for Preclinical AD

The goal of this study is to objectively test one's sense of smell, called olfaction, in participants with Subjective Cognitive Concerns (SCC), Mild Cognitive Impairment, Mild Behavioral Impairment (MBI), and age-matched controls. The main question it aims to answer is whether the AROMHA Brain Health Test could serve as a predictive biomarker of neurodegenerative disorders. This understanding will aid in the development of a noninvasive, cost-effective diagnostic tool that reliably and specifically distinguishes disease and normal aging populations. Participants will take the approximately 45-minute AROMHA Brain Health Smell Test where they will peel and sniff labels on the physical smell cards and answer questions on the web-based app relating to what they smelled. Participants will undergo tests for odor intensity, odor identification, odor discrimination, and episodic olfactory memory, but will not be provided the results of these tests.

The Signature of Alzheimer's Disease in Subjective Cognitive Decline

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.