scholarly journals 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

2019 ◽  
Author(s):  
Daniel Stamate ◽  
Min Kim ◽  
Petroula Proitsi ◽  
Sarah Westwood ◽  
Alison Baird ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medical Information ◽  
Biomarker Discovery ◽  
Gradient Boosting ◽  
Plasma Metabolites ◽  
Csf Biomarkers ◽  
Extreme Gradient Boosting ◽  
And Gender

AbstractINTRODUCTIONMachine 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.METHODSThis 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).RESULTSOn 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.DISCUSSIONThis 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

scholarly journals Sex-Specific Metabolic Pathways Were Associated with Alzheimer’s Disease (AD) Endophenotypes in the European Medical Information Framework for AD Multimodal Biomarker Discovery Cohort

Biomedicines ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1610
Author(s):  
Jin Xu ◽  
Rebecca Green ◽  
Min Kim ◽  
Jodie Lord ◽  
Amera Ebshiana ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Metabolic Pathways ◽  
Medical Information ◽  
Biomarker Discovery ◽  
Plasma Metabolites ◽  
Csf Biomarkers ◽  
Tryptophan Pathway ◽  
Personalised Treatment ◽  
Information Framework

Background: physiological differences between males and females could contribute to the development of Alzheimer's Disease (AD). Here, we examined metabolic pathways that may lead to precision medicine initiatives. Methods: We explored whether sex modifies the association of 540 plasma metabolites with AD endophenotypes including diagnosis, cerebrospinal fluid (CSF) biomarkers, brain imaging, and cognition using regression analyses for 695 participants (377 females), followed by sex-specific pathway overrepresentation analyses, APOE ε4 stratification and assessment of metabolites’ discriminatory performance in AD. Results: In females with AD, vanillylmandelate (tyrosine pathway) was increased and tryptophan betaine (tryptophan pathway) was decreased. The inclusion of these two metabolites (area under curve (AUC) = 0.83, standard error (SE) = 0.029) to a baseline model (covariates + CSF biomarkers, AUC = 0.92, SE = 0.019) resulted in a significantly higher AUC of 0.96 (SE = 0.012). Kynurenate was decreased in males with AD (AUC = 0.679, SE = 0.046). Conclusions: metabolic sex-specific differences were reported, covering neurotransmission and inflammation pathways with AD endophenotypes. Two metabolites, in pathways related to dopamine and serotonin, were associated to females, paving the way to personalised treatment.

scholarly journals TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer’s disease biomarker levels

2020 ◽  
Author(s):  
Shengjun Hong ◽  
Valerija Dobricic ◽  
Isabelle Bos ◽  
Stephanie J. B. Vos ◽  
Dmitry Prokopenko ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Medical Information ◽  
Genome Wide Association Study ◽  
Biomarker Discovery ◽  
Csf Biomarkers ◽  
Sequence Of Events ◽  
Genome Wide ◽  
Csf Levels

AbstractBackgroundNeurofilament light (NF-L), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are utilized as biomarkers for Alzheimer’s disease (AD), to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. Here we performed genome-wide association study (GWAS) analyses using all three biomarkers as outcome.MethodsDNA and cerebrospinal fluid (CSF) samples originated from the European Medical Information Framework AD Multimodal Biomarker Discovery (EMIF-AD MBD) study. Overlapping genotype/phenotype data were available for n=671 (NF-L), 677 (YKL-40), and 672 (Ng) individuals. GWAS analyses applied linear regression models adjusting for relevant covariates.FindingsWe identify novel genome-wide significant associations with markers in TMEM106B and CSF levels of NF-L. Additional novel signals were observed with DNA variants in CPOX and CSF levels of YKL-40. Lastly, we confirmed previous work suggesting that YKL-40 levels are regulated by cis protein quantitative trait loci (pQTL) in CHI3L1.InterpretationOur study provides important new insights into the genetic architecture underlying inter-individual variation in all three tested AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.

[P2-212]: EUROPEAN MEDICAL INFORMATION FRAMEWORK FOR ALZHEIMER's DISEASE (EMIF-AD): THE BIOMARKER DISCOVERY STUDY

2017 ◽  
Vol 13 (7S_Part_14) ◽  
pp. P691-P692 ◽  
Author(s):  
Isabelle Bos ◽  
Stephanie J.B. Vos ◽  
Rik Vandenberghe ◽  
Philip Scheltens ◽  
Sebastiaan Engelborghs ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Medical Information ◽  
Biomarker Discovery ◽  
Information Framework

scholarly journals Applied machine learning in Alzheimer's disease research: omics, imaging, and clinical data

2021 ◽  
Author(s):  
Ziyi Li ◽  
Xiaoqian Jiang ◽  
Yizhuo Wang ◽  
Yejin Kim
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Treatment Strategies ◽  
Drug Repurposing ◽  
Modern Technology ◽  
Disease Classification ◽  
Future Research ◽  
New Biomarkers

Alzheimer's disease (AD) remains a devastating neurodegenerative disease with few preventive or curative treatments available. Modern technology developments of high-throughput omics platforms and imaging equipment provide unprecedented opportunities to study the etiology and progression of this disease. Meanwhile, the vast amount of data from various modalities, such as genetics, proteomics, transcriptomics, and imaging, as well as clinical features impose great challenges in data integration and analysis. Machine learning (ML) methods offer novel techniques to address high dimensional data, integrate data from different sources, model the etiological and clinical heterogeneity, and discover new biomarkers. These directions have the potential to help us better manage the disease progression and develop novel treatment strategies. This mini-review paper summarizes different ML methods that have been applied to study AD using single-platform or multi-modal data. We review the current state of ML applications for five key directions of AD research: disease classification, drug repurposing, subtyping, progression prediction, and biomarker discovery. This summary provides insights about the current research status of ML-based AD research and highlights potential directions for future research.

scholarly journals Neuroinflammation and Alzheimer’s Disease: A Machine Learning Approach to CSF Proteomics

Cells ◽  
2021 ◽  
Vol 10 (8) ◽  
pp. 1930
Author(s):  
Lorenzo Gaetani ◽  
Giovanni Bellomo ◽  
Lucilla Parnetti ◽  
Kaj Blennow ◽  
Henrik Zetterberg ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Biomarker Discovery ◽  
Neurological Diseases ◽  
Univariate Analysis ◽  
Learning Approach ◽  
Barrier Dysfunction ◽  
Csf Analysis ◽  
Machine Learning Approach

In Alzheimer’s disease (AD), the contribution of pathophysiological mechanisms other than amyloidosis and tauopathy is now widely recognized, although not clearly quantifiable by means of fluid biomarkers. We aimed to identify quantifiable protein biomarkers reflecting neuroinflammation in AD using multiplex proximity extension assay (PEA) testing. Cerebrospinal fluid (CSF) samples from patients with mild cognitive impairment due to AD (AD-MCI) and from controls, i.e., patients with other neurological diseases (OND), were analyzed with the Olink Inflammation PEA biomarker panel. A machine-learning approach was then used to identify biomarkers discriminating AD-MCI (n: 34) from OND (n: 25). On univariate analysis, SIRT2, HGF, MMP-10, and CXCL5 showed high discriminatory performance (AUC 0.809, p = 5.2 × 10−4, AUC 0.802, p = 6.4 × 10−4, AUC 0.793, p = 3.2 × 10−3, AUC 0.761, p = 2.3 × 10−3, respectively), with higher CSF levels in AD-MCI patients as compared to controls. These same proteins were the best contributors to the penalized logistic regression model discriminating AD-MCI from controls (AUC of the model 0.906, p = 2.97 × 10−7). The biological processes regulated by these proteins include astrocyte and microglia activation, amyloid, and tau misfolding modulation, and blood-brain barrier dysfunction. Using a high-throughput multiplex CSF analysis coupled with a machine-learning statistical approach, we identified novel biomarkers reflecting neuroinflammation in AD. Studies confirming these results by means of different assays are needed to validate PEA as a multiplex technique for CSF analysis and biomarker discovery in the field of neurological diseases.

Associations of Green Tea Consumption and Cerebrospinal Fluid Biomarkers of Alzheimer’s Disease Pathology in Cognitively Intact Older Adults: The CABLE Study

2020 ◽  
Vol 77 (1) ◽  
pp. 411-421
Author(s):  
Ya-Hui Ma ◽  
Jia-Huan Wu ◽  
Wei Xu ◽  
Xue-Ning Shen ◽  
Hui-Fu Wang ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Cerebrospinal Fluid ◽  
Green Tea ◽  
Interaction Effects ◽  
Csf Biomarkers ◽  
Tea Consumption ◽  
And Gender ◽  
T Tau ◽  
Cognitively Intact

Background: Green tea has been widely recognized in ameliorating cognitive impairment and Alzheimer’s disease (AD), especially the progression of cognitive dysfunction. But the underlying mechanism is still unclear. Objective: This study was designed to determine the role of green tea consumption in the association with cerebrospinal fluid (CSF) biomarkers of AD pathology and to ascertain whether specific population backgrounds showed the differences toward these relationships. Methods: Multivariate linear models analyzed the available data on CSF biomarkers and frequency of green tea consumption of 722 cognitively intact participants from the Chinese Alzheimer’s Biomarker and LifestylE (CABLE) database, and we additionally detected the interaction effects of tea consumption with APOE ɛ4 status and gender using a two-way analysis of covariance. Results: Frequent green tea consumption was associated with a decreased level of CSF total-tau protein (t-tau) (p = 0.041) but not with the levels of CSF amyloid-β 42 (Aβ42) and CSF phosphorylated tau. The more pronounced associations of green tea consumption with CSF t-tau (p = 0.007) and CSF t-tau/Aβ42 (p = 0.039) were observed in individuals aged 65 years or younger. Additionally, males with frequent green tea consumption had a significantly low level of CSF t-tau/Aβ42 and a modest trend toward decreased CSF t-tau. There were no interaction effects of green tea consumption with APOE ɛ4 and gender. Conclusion: Collectively, our findings consolidated the favorable effects of green tea on the mitigation of AD risk. The constituents of green tea may improve abnormal tau metabolism and are promising targets in interventions and drug therapies.

P3-062: Relationship of atherogenic lipid profiles and gender with CSF biomarkers in middle-aged adults at risk for Alzheimer's disease

2008 ◽  
Vol 4 ◽  
pp. T535-T535
Author(s):  
Cynthia M. Carlsson ◽  
Jodi Barnet ◽  
Hanna M. Blazel ◽  
Luigi Puglielli ◽  
Craig S. Atwood ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
At Risk ◽  
Lipid Profiles ◽  
Csf Biomarkers ◽  
Middle Aged ◽  
And Gender ◽  
Middle Aged Adults ◽  
Relationship Of ◽  
Atherogenic Lipid

scholarly journals A Machine Learning-Based Holistic Approach to Predict the Clinical Course of Patients within the Alzheimer’s Disease Spectrum

2021 ◽  
pp. 1-17
Author(s):  
Noemi Massetti ◽  
Mirella Russo ◽  
Raffaella Franciotti ◽  
Davide Nardini ◽  
Giorgio Mandolini ◽  
...  
Keyword(s):  
Machine Learning ◽  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Test Scores ◽  
Healthy Aging ◽  
Neuropsychological Test ◽  
Holistic Approach ◽  
Csf Biomarkers ◽  
Disease Spectrum ◽  
Age And Sex

Background: Alzheimer’s disease (AD) is a neurodegenerative condition driven by multifactorial etiology. Mild cognitive impairment (MCI) is a transitional condition between healthy aging and dementia. No reliable biomarkers are available to predict the conversion from MCI to AD. Objective: To evaluate the use of machine learning (ML) on a wealth of data offered by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and Alzheimer’s Disease Metabolomics Consortium (ADMC) database in the prediction of the MCI to AD conversion. Methods: We implemented an ML-based Random Forest (RF) algorithm to predict conversion from MCI to AD. Data related to the study population (587 MCI subjects) were analyzed by RF as separate or combined features and assessed for classification power. Four classes of variables were considered: neuropsychological test scores, AD-related cerebrospinal fluid (CSF) biomarkers, peripheral biomarkers, and structural magnetic resonance imaging (MRI) variables. Results: The ML-based algorithm exhibited 86% accuracy in predicting the AD conversion of MCI subjects. When assessing the features that helped the most, neuropsychological test scores, MRI data, and CSF biomarkers were the most relevant in the MCI to AD prediction. Peripheral parameters were effective when employed in association with neuropsychological test scores. Age and sex differences modulated the prediction accuracy. AD conversion was more effectively predicted in females and younger subjects. Conclusion: Our findings support the notion that AD-related neurodegenerative processes result from the concerted activity of multiple pathological mechanisms and factors that act inside and outside the brain and are dynamically affected by age and sex.

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