Digital Solutions for Predicting the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors

Официальное название

Development of Digital Solutions for the Prediction of the Biological Mechanisms of Alzheimer's Disease Through the Analysis of Risk Factors (PrevAI)

Обзор

Population ageing is one of the main factors responsible for the global increase in the prevalence of dementia. Recent evidence suggests that modifiable risk factors, such as cardiovascular disease and lifestyle, may increase the risk of developing dementia and contribute to its progression. Furthermore, the use of non-invasive plasma biomarkers enables the identification of individuals with neurodegenerative diseases, even in the prodromal stage. However, the relationship between the cumulative burden of risk factors and plasma biomarkers is still poorly understood. The main objective of this study is to identify and estimate the risk associated with modifiable and non-modifiable predictors (risk factors) linked to the development of Alzheimer's disease (AD) and non-AD dementia, as well as biological alterations consistent with AD or non-AD, through the development of a predictive tool based on Artificial Intelligence algorithms (Machine Learning model). The study also aims to provide a range of technological tools (an app for active patient monitoring and a web platform for clinicians) that could improve risk stratification and the personalisation of care pathways. The study is divided into two different phases. Firstly, a retrospective phase is conducted in order to construct a predictive model for the risk of dementia and biological alterations consistent with AD. Secondly, a prospective phase is performed for the validation of the predictive model.

Вмешательства

• Устройство Digital solution for the prediction of the biological mechanisms of Alzheimer's disease through the analysis of risk factors
  The intervention consists of a mobile application ("app") for risk monitoring with gamified patient engagement, which is design to support remote health monitoring and participant adherence to the study. Participants will enter informative clinical variables every 3 months, including weight, height, age, systolic/diastolic blood pressure, and blood glucose levels. The app generates a qualitative risk assessment (low/medium/high) based on entered data, intended as a clinical monitoring support to

Первичные конечные точки

• Conversion rate to dementia [Срок оценки: From enrollment to 3-6 months after enrollment]

Критерии участия

Критерии включения

• Male or female subjects aged more than 18 years at the time of signing the informed consent form;
• Subjects with MCI or SCD who, at the time of their first visit, did not have a clinical diagnosis of dementia (MMSE ≥ 24);
• Smartphone user.

Критерии исключения

• Age younger than that stated in the inclusion criterion;
• Inability to understand.

Критерии приведены из реестра в оригинале (на английском). Окончательную оценку соответствия проводит исследовательский центр.

Здоровые добровольцы: Нет

Дизайн исследования

Распределение

Не применимо

Модель

Одна группа

Маскирование

Открытое

Основная цель

Скрининг

Центры проведения

Италия · 2 центра

• ASST Spedali Civili di Brescia — Brescia
• IRCCS Istituto Centro San Giovanni Di Dio - Fatebenefratelli — Brescia

Публикации

• Redolfi A, Manset D, Barkhof F, Wahlund LO, Glatard T, Mangin JF, Frisoni GB; neuGRID Consortium, for the Alzheimer's Disease Neuroimaging Initiative. Head-to-head comparison of two popular cortical thickness extraction algorithms: a cross-sectional and longitudinal study. PLoS One. 2015 Mar 17;10(3):e0117692. doi: 10.1371/journal.pone.0117692. eCollection 2015. PMID 25781983
• Mitchell AJ, Shiri-Feshki M. Rate of progression of mild cognitive impairment to dementia--meta-analysis of 41 robust inception cohort studies. Acta Psychiatr Scand. 2009 Apr;119(4):252-65. doi: 10.1111/j.1600-0447.2008.01326.x. Epub 2008 Feb 18. PMID 19236314
• Martinez-Dubarbie F, Guerra-Ruiz A, Lopez-Garcia S, Lage C, Fernandez-Matarrubia M, Infante J, Pozueta-Cantudo A, Garcia-Martinez M, Corrales-Pardo A, Bravo M, Lopez-Hoyos M, Irure-Ventura J, Valeriano-Lorenzo E, Garcia-Unzueta MT, Sanchez-Juan P, Rodriguez-Rodriguez E. Diagnostic Accuracy of Plasma p-tau217 for Detecting Pathological Cerebrospinal Fluid Changes in Cognitively Unimpaired Subjects PMID 39559871
• Sathyan A, Weinberg AI, Cohen K. Interpretable AI for bio-medical applications. Complex Eng Syst. 2022 Dec;2(4):18. doi: 10.20517/ces.2022.41. Epub 2022 Dec 28. PMID 37025127
• Padovani A, Galli A, Bazzoli E, Tolassi C, Caratozzolo S, Gumina B, Benussi A, Libri I, Outeiro TF, Pilotto A. The role of insulin resistance and APOE genotype on blood-brain barrier integrity in Alzheimer's disease. Alzheimers Dement. 2025 Feb;21(2):e14556. doi: 10.1002/alz.14556. PMID 39992249
• Jessen F, Amariglio RE, Buckley RF, van der Flier WM, Han Y, Molinuevo JL, Rabin L, Rentz DM, Rodriguez-Gomez O, Saykin AJ, Sikkes SAM, Smart CM, Wolfsgruber S, Wagner M. The characterisation of subjective cognitive decline. Lancet Neurol. 2020 Mar;19(3):271-278. doi: 10.1016/S1474-4422(19)30368-0. Epub 2020 Jan 17. PMID 31958406
• McKeith IG, Ferman TJ, Thomas AJ, Blanc F, Boeve BF, Fujishiro H, Kantarci K, Muscio C, O'Brien JT, Postuma RB, Aarsland D, Ballard C, Bonanni L, Donaghy P, Emre M, Galvin JE, Galasko D, Goldman JG, Gomperts SN, Honig LS, Ikeda M, Leverenz JB, Lewis SJG, Marder KS, Masellis M, Salmon DP, Taylor JP, Tsuang DW, Walker Z, Tiraboschi P; prodromal DLB Diagnostic Study Group. Research criteria for the d PMID 32241955
• Albert MS, DeKosky ST, Dickson D, Dubois B, Feldman HH, Fox NC, Gamst A, Holtzman DM, Jagust WJ, Petersen RC, Snyder PJ, Carrillo MC, Thies B, Phelps CH. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 2011 May;7(3):270- PMID 21514249

Идентификаторы

NCT: NCT07731191 · PrevAl

Первоисточники (государственные реестры)

• ClinicalTrials.gov (NIH, США)

Открыть это исследование на ClinicalTrials.gov ↗