A metabolite-based machine learning approach to diagnose Alzheimer’s-type dementia in blood: Results from the European Medical Information Framework for Alzheimer’s Disease biomarker discovery cohort

Abstract

INTRODUCTION: Machine learning (ML) may harbor the potential to capture the metabolic complexity in Alzheimer's Disease (AD). Here we set out to test the performance of metabolites in blood to categorise AD when compared to CSF biomarkers. METHODS: This study analysed samples from 242 cognitively normal (CN) people and 115 with AD-type dementia utilizing plasma metabolites (n=883). Deep Learning (DL), Extreme Gradient Boosting (XGBoost) and Random Forest (RF) were used to differentiate AD from CN. These models were internally validated using Nested Cross Validation (NCV). RESULTS: On the test data, DL produced the AUC of 0.85 (0.80-0.89), XGBoost produced 0.88 (0.86-0.89) and RF produced 0.85 (0.83-0.87). By comparison, CSF measures of amyloid, p-tau and t-tau (together with age and gender) produced with XGBoost the AUC values of 0.78, 0.83 and 0.87, respectively. DISCUSSION: This study showed that plasma metabolites have the potential to match the AUC of well-established AD CSF biomarkers in a relatively small cohort. Further studies in independent cohorts are needed to validate whether this specific panel of blood metabolites can separate AD from controls, and how specific it is for AD as compared with other neurodegenerative disorders.

Applying Deep Learning to Predicting Dementia and Mild Cognitive Impairment

“…According to literature, there are hundreds of possible predictors for dementia, which can generally be categorized based on the following types of models: neuropsychological based models, health-based models, multifactorial models and genetic risk scores [19]. The applicability of these models spreads in multiple directions [16,20,21]. The magnetic resonance imaging (MRI), in combination with multiplex neural networks, have been used to segregate healthy brains from progressive mild cognitive impairment (pMCI), based on the structural atrophy of the brain because of Alzheimer's disease [20].…”

“…Positron emission tomography (PET) scans and the regional analysis of the protein amyloid-β, have been used by a Random Forest classifier to identify patients with age-related stable MCI and pMCI [22]. In a recent EMIF-AD study [16], a machine learning methodology based on Extreme Gradient Boosting XGBoost, Random Forest and Deep Learning, has been proposed for Alzheimer's based dementia diagnosis using metabolites in the blood which were proven by the study to be as accurate predictors as the widely accepted but invasive to measure cerebrospinal fluid (CSF) biomarkers.…”

Predicting risk of dementia with machine learning and survival models using routine primary care records

“…Positron emission tomography scans and the regional analysis of the protein amyloid-β, have been used by a Random Forest classifier to identify patients with age-related stable mild MCI and pMCI [8]. Blood metabolites measurements data samples can be used to predict Alzheimer's dementia with powerful predictive models such as XGBoost, at least as well as with using the well-established but much more invasive to measure biomarkers based on the cerebrospinal fluid (CSF) [16].…”