Journal Article

Multipredictor risk models for predicting individual risk of Alzheimer’s disease

Suckling, John, Wang, Hui-Fu, Li, Hong-Qi, Dong, Qiang, Zuo, Chuan-Tao, Shen, Xue-Ning, Tan, Lan, Cui, Mei, Yu, Jin-Tai, Huang, Yu-Yuan, Liu, Yong, Hou, Xiao-He, Tan, Chen-Chen

2023

Overview

Background Early prevention of Alzheimer’s disease (AD) is a feasible way to delay AD onset and progression. Information on AD prediction at the individual patient level will be useful in AD prevention. In this study, we aim to develop risk models for predicting AD onset at individual level using optimal set of predictors from multiple features. Methods A total of 487 cognitively normal (CN) individuals and 796 mild cognitive impairment (MCI) patients were included from Alzheimer's Disease Neuroimaging Initiative. All the participants were assessed for clinical, cognitive, magnetic resonance imaging and cerebrospinal fluid (CSF) markers and followed for mean periods of 5.6 years for CN individuals and 4.6 years for MCI patients to ascertain progression from CN to incident prodromal stage of AD or from MCI to AD dementia. Least Absolute Shrinkage and Selection Operator Cox regression was applied for predictors selection and model construction. Results During the follow-up periods, 139 CN participants had progressed to prodromal AD (CDR ≥ 0.5) and 321 MCI patients had progressed to AD dementia. In the prediction of individual risk of incident prodromal stage of AD in CN individuals, the AUC of the final CN model was 0.81 within 5 years. The final MCI model predicted individual risk of AD dementia in MCI patients with an AUC of 0.92 within 5 years. The models were also associated with longitudinal change of Mini-Mental State Examination (p < 0.001 for CN and MCI models). An Alzheimer’s continuum model was developed which could predict the Alzheimer’s continuum for individuals with normal AD biomarkers within 3 years with high accuracy (AUC = 0.91). Conclusions The risk models were able to provide personalized risk for AD onset at each year after evaluation. The models may be useful for better prevention of AD.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Advertising executives / Alzheimer Disease / Alzheimer's disease / Biomarkers / Biomedical and Life Sciences / Biomedicine / Cerebrospinal fluid / Cognition / Cognitive ability / Cognitive Dysfunction - complications / Cognitive Dysfunction - diagnosis / Cognitive Dysfunction - pathology / Computational Modelling and Epidemiology / Datasets / Dementia / Dementia disorders / Demographics / Disease prevention / Disease Progression / Enzymes / Humans / Magnetic resonance imaging / Medical imaging / Medical research / Medicine, Experimental / Medicine/Public Health / Memory / Monoclonal antibodies / Neurodegenerative diseases / Neuroimaging / Neuropsychology / Patients / Prediction / Prodromal Symptoms / Risk assessment / Risk factors / Risk model