With the increasing prevalence of Alzheimer’s disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.
Con l’aumento della prevalenza della malattia di Alzheimer (AD) nelle popolazioni che invecchiano e le limitate opzioni terapeutiche disponibili per rallentarne o invertirne la progressione, non è mai stata così urgente la necessità di strumenti diagnostici migliorati per identificare i pazienti nelle fasi precliniche e prodromiche dell’AD. I modelli biofisici della diffusione basata sul connettoma dell’amiloide-beta (Aβ) e della proteina tau (τ) associata ai microtubuli hanno recentemente ottenuto buoni risultati come strumenti predittivi dell’andamento temporale delle alterazioni patologiche correlate all’AD. Tuttavia, data la complessa eziologia dell’AD — che coinvolge non solo la diffusione della patologia proteica mediata dal connettoma, ma anche le interazioni di numerosi attori molecolari e cellulari su molteplici scale spazio-temporali — sono necessari modelli biofisici più robusti e completi per comprendere meglio la fisiopatologia dell’AD e, in ultima analisi, fornire diagnosi e prognosi accurate e specifiche per il singolo paziente. In questo lavoro discutiamo diverse aree di ricerca attiva nell’ambito dell’AD, le cui conoscenze possono essere utilizzate per migliorare la modellizzazione matematica della patologia dell’AD, nonché i recenti tentativi di sviluppare modelli biofisici basati sul connettoma più avanzati. Questi sforzi verso una descrizione matematica dell’AD che sia al contempo completa e parsimoniosa offrono grandi prospettive per migliorare sia la diagnosi dei pazienti a rischio di AD sia la comprensione dei meccanismi con cui la malattia progredisce.
Modelli biofisici basati sul connettoma per la diagnosi e la prognosi della malattia di Alzheimer
DEL MONTE, SERENA
2024/2025
Abstract
With the increasing prevalence of Alzheimer’s disease (AD) among aging populations and the limited therapeutic options available to slow or reverse its progression, the need has never been greater for improved diagnostic tools for identifying patients in the preclinical and prodomal phases of AD. Biophysics models of the connectome-based spread of amyloid-beta (Aβ) and microtubule-associated protein tau (τ) have enjoyed recent success as tools for predicting the time course of AD-related pathological changes. However, given the complex etiology of AD, which involves not only connectome-based spread of protein pathology but also the interactions of many molecular and cellular players over multiple spatiotemporal scales, more robust, complete biophysics models are needed to better understand AD pathophysiology and ultimately provide accurate patient-specific diagnoses and prognoses. Here we discuss several areas of active research in AD whose insights can be used to enhance the mathematical modeling of AD pathology as well as recent attempts at developing improved connectome-based biophysics models. These efforts toward a comprehensive yet parsimonious mathematical description of AD hold great promise for improving both the diagnosis of patients at risk for AD and our mechanistic understanding of how AD progresses.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12075/25285