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

Stamate, Daniel; Kim, Min; Proitsi, Petroula; Westwood, Sarah; Baird, Alison L.; Nevado‐Holgado, Alejo J.; Hye, Abdul; Bos, Isabelle; Vos, Stephanie J.B.; Vandenberghe, Rik; Teunissen, Charlotte E.; Kate, Mara ten; Scheltens, Philip; Gabel, Silvy; Meersmans, Karen; Blin, Olivier; Richardson, Jill C.; Roeck, Ellen De; Engelborghs, Sebastiaan; Sleegers, Kristel; Bordet, Régis; Ramit, Lorena; Kettunen, Petronella; Tsolaki, Magda; Verhey, Frans R.J.; Alcolea, Daniel; Lleó, Alberto; Peyratout, Gwendoline; Tainta, Mikel; Johannsen, Peter; Freund‐Levi, Yvonne; Frölich, Lutz; Dobričić, Valerija; Frisoni, Giovanni B.; Molinuevo, José Luís; Wallin, Anders; Popp, Julius; Martínez-Lage, Pablo; Bertram, Lars; Blennow, Kaj; Zetterberg, Henrik; Streffer, Johannes; Visser, Pieter Jelle; Lovestone, Simon; Legido‐Quigley, Cristina

doi:10.1016/j.trci.2019.11.001

Cited by 83 publications

(70 citation statements)

References 24 publications

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“…In addition, we have also previously shown that soluble intracellular cell adhesion molecule-1 in CSF is associated with AD (18). At metabolite level, we identi ed 10 molecules in CSF associated with Tau and P-Tau, which differ from the blood biomarkers associated with AD identi ed in a recent study in a large sample (35). Overall, our approach identi ed more molecules associated with AD pathology as compared to previous studies.…”

Section: Discussionmentioning

confidence: 55%

An Integrative Multi-omics Approach Reveals New Central Nervous System Pathway Alterations in Alzheimer’s Disease

Clark

Dayon

Masoodi

et al. 2020

Preprint

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Background: Multiple pathophysiological processes have been described in Alzheimer’s disease (AD). Their inter-individual variations, complex interrelations, and relevance for clinical manifestation and disease progression remain poorly understood, however. We tested the hypothesis that cerebrospinal fluid (CSF) integrative multi-omics analysis highlights novel interacting pathway alterations in AD.Methods: We performed multi-level CSF omics in a well-characterized cohort of older adults including subjects with normal cognition, mild cognitive impairment, and mild dementia. Proteomics, metabolomics, lipidomics, one-carbon metabolism, and neuroinflammation related molecules were analysed applying Elastic-net regression and Multi-Omics Factor Analysis followed by pathway enrichment. Multivariate analysis was used to select best predictive models of AD pathology and cognitive decline.Results: Multi-omics integration identified five major dimensions of heterogenicity explaining the variance within the cohort and differentially associated with AD . Further analysis exposed multiple interactions between single ‘omics modalities and distinct multi-omics molecular signatures differentially related to amyloid pathology, neuronal injury, and tau hyperphosphorylation. Enrichment pathway analysis revealed overrepresentation of the hemostasis, immune response and extracellular matrix signalling pathways in association with AD. Further, combinations of four selected molecules significantly improved prediction of both AD (protein 14-3-3 zeta/delta, clusterin, interleukin-15, and transgelin-2) and cognitive decline (protein 14-3-3 zeta/delta, clusterin, cholesteryl ester 27:1 16:0 and monocyte chemoattractant protein-1). Conclusions: Applying an integrative multi-omics approach we confirmed previously reported associations with AD pathology and report new molecular and pathways alterations. These findings are relevant for the development of personalized diagnosis and treatment approaches in AD.

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Section: Discussionmentioning

confidence: 55%

An Integrative Multi-omics Approach Reveals New Central Nervous System Pathway Alterations in Alzheimer’s Disease

Clark

Dayon

Masoodi

et al. 2020

Preprint

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“…Comparing our results with the three recent relevant studies (see Table V), we note that the panels identified in [25] and [26] classified ADD and HC with high performance, but the markers were reported by the authors to be poor at distinguishing between MCI and HC. Furthermore, while study [27] achieved high AUC of 0.88 with XGBoost model for classification of ADD and HC, the model's performance has not been evaluated for disease detection at MCI stage. Due to unavailability of biomarkers used in the study in our study data, the performance of the models for MCI and HC classification was not investigated in this study.…”

Section: Discussionmentioning

confidence: 98%

“…In another study, a 12-marker panel classified ADD and HC with 90% SN and 66.7% specificity, and higher performance in post-mortem confirmed AD cases [26]. Furthermore, a study [27] that explored the use of deep learning, random forest, and XGBoost algorithms for classification of ADD and HC achieved AUC of 0.88 with XGBoost algorithm and 0.85 with deep learning and random forest. Despite the promising results from these studies, most of the models were developed and evaluated using data from cognitively healthy controls and subjects at the later stages of the disease.…”

Section: Introductionmentioning

confidence: 98%

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Eke

Jammeh

et al. 2021

IEEE J. Biomed. Health Inform.

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The successful development of amyloid-based biomarkers and tests for Alzheimer's disease (AD) represents an important milestone in AD diagnosis. However, two major limitations remain. Amyloid-based diagnostic biomarkers and tests provide limited information about the disease process and they are unable to identify individuals with the disease before significant amyloid-beta accumulation in the brain develops. The objective in this study is to develop a method to identify potential blood-based non-amyloid biomarkers for early AD detection. The use of blood is attractive because it is accessible and relatively inexpensive. Our method is mainly based on machine learning (ML) techniques (support vector machines in particular) because of their ability to create multivariable models by learning patterns from complex data. Using novel feature selection and evaluation modalities we identified 5 novel panels of non-amyloid proteins with the potential to serve as biomarkers of early AD. In particular, we found that the combination of A2M, ApoE, BNP, Eot3, RAGE and SGOT may be a key biomarker profile of early disease. Disease detection models based on the identified panels achieved sensitivity (SN) > 80%, specificity (SP) > 70%, and area under receiver operating curve (AUC) of at least 0.80 at prodromal stage (with higher performance at later stages) of the disease. Existing ML models performed poorly in comparison at this stage of the disease suggesting that the underlying protein panels may not be suitable for early disease detection. Our results demonstrate the feasibility of early detection of AD using nonamyloid based biomarkers.

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“…Interestingly, Stamate et al, using a 883-feature metabolomic dataset produced three biosignatures: one with AUC 0.850 (0.800–0.890) via Deep learning, a second of AUC 0.880 (0.860–0.890) via an XGBoost model and a last of AUC 0.850 (0.830–0.870) via an Random Forest model, respectively. However, all those biosignatures contained as much as 347 predictors [ 30 ].…”

Section: Discussionmentioning

confidence: 99%

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

Karaglani

Gourlia

Tsamardinos

et al. 2020

JCM

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Alzheimer’s disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age–sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.

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

Cited by 83 publications

References 24 publications

An Integrative Multi-omics Approach Reveals New Central Nervous System Pathway Alterations in Alzheimer’s Disease

An Integrative Multi-omics Approach Reveals New Central Nervous System Pathway Alterations in Alzheimer’s Disease

Early Detection of Alzheimer's Disease with Blood Plasma Proteins Using Support Vector Machines

Accurate Blood-Based Diagnostic Biosignatures for Alzheimer’s Disease via Automated Machine Learning

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