BackgroundAs disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.MethodsWe will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of A & beta;(42), t-tau, and p-tau concentration and A & beta;(42)/A & beta;(40) ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.DiscussionThis is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol / Mazzeo, Salvatore; Lassi, Michael; Padiglioni, Sonia; Vergani, Alberto Arturo; Moschini, Valentina; Scarpino, Maenia; Giacomucci, Giulia; Burali, Rachele; Morinelli, Carmen; Fabbiani, Carlo; Galdo, Giulia; Amato, Lorenzo Gaetano; Bagnoli, Silvia; Emiliani, Filippo; Ingannato, Assunta; Nacmias, Benedetta; Sorbi, Sandro; Grippo, Antonello; Mazzoni, Alberto; Bessi, Valentina. - In: BMC NEUROLOGY. - ISSN 1471-2377. - ELETTRONICO. - 23:(2023), pp. 300.0-300.0. [10.1186/s12883-023-03347-8]

PRedicting the EVolution of SubjectIvE Cognitive Decline to Alzheimer’s Disease With machine learning: the PREVIEW study protocol

Mazzeo, Salvatore;Padiglioni, Sonia;Scarpino, Maenia;Giacomucci, Giulia;Fabbiani, Carlo;Galdo, Giulia;Bagnoli, Silvia;Emiliani, Filippo;Ingannato, Assunta;Nacmias, Benedetta;Sorbi, Sandro;Grippo, Antonello;Bessi, Valentina
2023

Abstract

BackgroundAs disease-modifying therapies (DMTs) for Alzheimer's disease (AD) are becoming a reality, there is an urgent need to select cost-effective tools that can accurately identify patients in the earliest stages of the disease. Subjective Cognitive Decline (SCD) is a condition in which individuals complain of cognitive decline with normal performances on neuropsychological evaluation. Many studies demonstrated a higher prevalence of Alzheimer's pathology in patients diagnosed with SCD as compared to the general population. Consequently, SCD was suggested as an early symptomatic phase of AD. We will describe the study protocol of a prospective cohort study (PREVIEW) that aim to identify features derived from easily accessible, cost-effective and non-invasive assessment to accurately detect SCD patients who will progress to AD dementia.MethodsWe will include patients who self-referred to our memory clinic and are diagnosed with SCD. Participants will undergo: clinical, neurologic and neuropsychological examination, estimation of cognitive reserve and depression, evaluation of personality traits, APOE and BDNF genotyping, electroencephalography and event-related potential recording, lumbar puncture for measurement of A & beta;(42), t-tau, and p-tau concentration and A & beta;(42)/A & beta;(40) ratio. Recruited patients will have follow-up neuropsychological examinations every two years. Collected data will be used to train a machine learning algorithm to define the risk of being carriers of AD and progress to dementia in patients with SCD.DiscussionThis is the first study to investigate the application of machine learning to predict AD in patients with SCD. Since all the features we will consider can be derived from non-invasive and easily accessible assessments, our expected results may provide evidence for defining cost-effective and globally scalable tools to estimate the risk of AD and address the needs of patients with memory complaints. In the era of DMTs, this will have crucial implications for the early identification of patients suitable for treatment in the initial stages of AD.
2023
23
0
0
Goal 3: Good health and well-being
Mazzeo, Salvatore; Lassi, Michael; Padiglioni, Sonia; Vergani, Alberto Arturo; Moschini, Valentina; Scarpino, Maenia; Giacomucci, Giulia; Burali, Rachele; Morinelli, Carmen; Fabbiani, Carlo; Galdo, Giulia; Amato, Lorenzo Gaetano; Bagnoli, Silvia; Emiliani, Filippo; Ingannato, Assunta; Nacmias, Benedetta; Sorbi, Sandro; Grippo, Antonello; Mazzoni, Alberto; Bessi, Valentina
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1355973
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