A Comprehensive Machine Learning Framework for the Exact Prediction of the Age of Onset in Familial and Sporadic Alzheimer's Disease.

Jorge I Vélez,Mauricio Arcos-Holzinger,Mauricio Arcos-Burgos,Luiggi A Samper,Lady G Espinosa,Mario A Isaza-Ruget,Francisco Lopera

doi:10.3390/diagnostics11050887

Abstract

Machine learning (ML) algorithms are widely used to develop predictive frameworks. Accurate prediction of Alzheimer’s disease (AD) age of onset (ADAOO) is crucial to investigate potential treatments, follow-up, and therapeutic interventions. Although genetic and non-genetic factors affecting ADAOO were elucidated by other research groups and ours, the comprehensive and sequential application of ML to provide an exact estimation of the actual ADAOO, instead of a high-confidence-interval ADAOO that may fall, remains to be explored. Here, we assessed the performance of ML algorithms for predicting ADAOO using two AD cohorts with early-onset familial AD and with late-onset sporadic AD, combining genetic and demographic variables. Performance of ML algorithms was assessed using the root mean squared error (RMSE), the R-squared (R2), and the mean absolute error (MAE) with a 10-fold cross-validation procedure. For predicting ADAOO in familial AD, boosting-based ML algorithms performed the best. In the sporadic cohort, boosting-based ML algorithms performed best in the training data set, while regularization methods best performed for unseen data. ML algorithms represent a feasible alternative to accurately predict ADAOO with little human intervention. Future studies may include predicting the speed of cognitive decline in our cohorts using ML.

Highlights

Alzheimer’s disease (AD; OMIM 104300) is a neurodegenerative disorder characterized by progressive loss of neurological, mental, and cognitive functions, including memory, changes in judgment, behavior, and emotions [1,2,3,4]
AD is divided into familial AD (f AD), which accounts for 90% of AD cases
We previously studied the association of common exonic functional variants (CEFVs) with ADAOO (Table 1) [35,36] using single- and multi-locus linear mixed-effects models [77] and recursive partitioning Machine learning (ML) algorithms [36]

Summary

Introduction

Alzheimer’s disease (AD; OMIM 104300) is a neurodegenerative disorder characterized by progressive loss of neurological, mental, and cognitive functions, including memory, changes in judgment, behavior, and emotions [1,2,3,4]. Other ML alternatives include the use of artificial intelligence (AI), namely deep learning (DL), assessing AD diagnosis and progression with brain radiological images [69,70] These results are promising, their main limitation is that the predictive model provided either an estimate of the risk of an individual for developing AD or the range within which the ADAOO may fall with high confidence (i.e., early- or late-onset based on whether the ADAOO was before or after a threshold, respectively), but not an estimate of the actual ADAOO. Our results suggest that ML constitutes a feasible and easy-to-implement new methodology to predict ADAOO, especially in the clinical setting, while significantly overpowering our previous results and paving the way for new possibilities to define follow-up and counseling strategies for patients and their family members

Materials and Methods

Variants Associated with ADAOO

ADAOO Prediction Using ML

ADAOO Prediction in the fAD E280A Pedigree

Variable Importance