COMPASS: A computational model to predict changes in MMSE scores 24-months after initial assessment of Alzheimer's disease.

Fan Zhu,Benjamin M Hampstead,Henry L Paulson,Roger L Albin,Hiroko H Dodge,Yuanfang Guan,Bharat Panwar,Hongdong Li

doi:10.1038/srep34567

Abstract

We present COMPASS, a COmputational Model to Predict the development of Alzheimer’s diSease Spectrum, to model Alzheimer’s disease (AD) progression. This was the best-performing method in recent crowdsourcing benchmark study, DREAM Alzheimer’s Disease Big Data challenge to predict changes in Mini-Mental State Examination (MMSE) scores over 24-months using standardized data. In the present study, we conducted three additional analyses beyond the DREAM challenge question to improve the clinical contribution of our approach, including: (1) adding pre-validated baseline cognitive composite scores of ADNI-MEM and ADNI-EF, (2) identifying subjects with significant declines in MMSE scores, and (3) incorporating SNPs of top 10 genes connected to APOE identified from functional-relationship network. For (1) above, we significantly improved predictive accuracy, especially for the Mild Cognitive Impairment (MCI) group. For (2), we achieved an area under ROC of 0.814 in predicting significant MMSE decline: our model has 100% precision at 5% recall, and 91% accuracy at 10% recall. For (3), “genetic only” model has Pearson’s correlation of 0.15 to predict progression in the MCI group. Even though addition of this limited genetic model to COMPASS did not improve prediction of progression of MCI group, the predictive ability of SNP information extended beyond well-known APOE allele.

Highlights

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder accounting for approximately 60% of dementia cases[1]
The primary goal of ADNI is to test whether serial magnetic resonance imaging (MRI), positron emission tomography (PET), other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of mild cognitive impairment (MCI) and early Alzheimer’s disease (AD)
Because of differences between AD, Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) groups, we developed separate models to predict delta Mini-Mental State Examination (MMSE) for each group

Summary

Introduction

Alzheimer’s disease (AD) is a chronic neurodegenerative disorder accounting for approximately 60% of dementia cases[1]. Use of multimodal biomarkers may not be feasible for clinical practice and is inconvenient for clinical research It was on this background of prior studies that the Dialogue for Reverse Engineering And Methods (DREAM) Alzheimer’s Disease Big Data challenge[14] was organized to identify efficient and comparable methods using a ‘wisdom of the crowd’ approach. In this Challenge competition participants were asked to come up with an algorithm which predicts the decline in Mini-Mental State Examination (MMSE) scores over 24 months using a pre-specified data set. In an effort to improve COMPASS and to explore personalized predictions, we further included cognitive composite scores as predictors of MMSE declines, and evaluated the feasibility of using genetic information other than APOE to predict delta MMSE in the MCI population

Methods

Results

Conclusion