Molecular Subtyping of Mild Cognitive Impairment Based on Genetic Polymorphism and Gene Expression

Li, H.-T.; Yuan, Shaoxun; Wu, Jiansheng; Zhang, X.-Z.; Liu, Y.; Sun, Xiao

doi:10.14283/jpad.2020.65

Cited by 2 publications

(7 citation statements)

References 28 publications

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“…In our previous study, we applied the SNF method to identify the subtypes of MCI patients in the ADNI-1 dataset, based on integrating genetic polymorphism and gene expression data [ 10 ]. Two MCI subtypes were identified based on our method and were compared by the following factors: the time difference of the conversion from MCI to AD, cognitive scales and sMRI images and significantly enriched pathways based on differentially expressed genes separately.…”

Section: Resultsmentioning

confidence: 99%

“…In this paper, the MCI-CPS model was applied to predict MCI-to-AD conversion in three years using multi-modality data, including structural MRI, genotype data and gene expression profiling, based on patients’ MCI subtype. In a previous study [ 10 ], we performed SNF, an integrative clustering of multiple genomic data algorithms, to cluster MCI patients. We conducted experimental studies on subtypes of MCI patients and showed that omics data can define subtypes characterized by biological and clinical significance.…”

Section: Discussionmentioning

confidence: 99%

“…First, subtype-specific classifiers are more accurate than classification without subtyping (AUC of 0.8581 for subtype I sub-classifier, 0.8623 for subtype II sub-classifier and 0.7849 for raw classifier). Because distinct molecular subtypes of MCI can be identified with different clinical and biological outcomes [ 10 ], identifying the MCI patient subtypes will improve the performance of predicting the conversion from MCI to AD [ 9 ]. This is important for personalized and precision medicine.…”

Section: Discussionmentioning

confidence: 99%

“…In our previous research [ 10 ], we identified two MCI subtypes based on SNF in ADNI-1 and we applied the label propagation algorithm to assign a subtype label to each MCI patient in the ADNI-GO/2 dataset.…”

Section: Methodsmentioning

confidence: 99%

“…In a previous study, we constructed a method to identify the subtypes of MCI patients based on integrating genetic polymorphism and gene expression data [ 10 ]. The results of subtyping patients based on our method have biological and clinical significance.…”

Section: Introductionmentioning

confidence: 99%

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Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype

Yuan

et al. 2021

Brain Sciences

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Alzheimer’s disease (AD) is a neurodegenerative brain disease in the elderly. Identifying patients with mild cognitive impairment (MCI) who are more likely to progress to AD is a key step in AD prevention. Recent studies have shown that AD is a heterogeneous disease. In this study, we propose a subtyping-based prediction strategy to predict the conversion from MCI to AD in three years according to MCI patient subtypes. Structural magnetic resonance imaging (sMRI) data and multi-omics data, including genotype data and gene expression profiling derived from peripheral blood samples, from 125 MCI patients were used in the Alzheimer’s Disease Neuroimaging Initiative (ADNI)-1 dataset and from 98 MCI patients in the ADNI-GO/2 dataset. A variational Bayes approximation model based on the multiple kernel learning method was constructed to predict whether an MCI patient will progress to AD within three years. In internal fivefold cross-validation within ADNI-1, we achieved an overall AUC of 0.83 (79.20% accuracy, 81.25% sensitivity, 77.92% specificity) compared to the model without subtyping, which achieved an AUC of 0.78 (76.00% accuracy, 77.08% sensitivity, 75.32% specificity). In external validation using ADNI-1 as a training set and ADNI-GO/2 as an independent test set, we attained an AUC of 0.78 (74.49% accuracy, 74.19% sensitivity, 74.63% specificity). Identifying MCI patient subtypes with omics data would improve the accuracy of predicting the conversion from MCI to AD. In addition to evaluating statistics, obtaining the significant sMRI, single nucleotide polymorphism (SNP) and mRNA expression data from peripheral blood of MCI patients is noninvasive and cost-effective for predicting conversion from MCI to AD.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype

Yuan

et al. 2021

Brain Sciences

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Comorbidity‐driven multi‐modal subtype analysis in mild cognitive impairment of Alzheimer's disease

Katabathula

Davis

2022

Alzheimer's & Dementia

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Background Mild cognitive impairment (MCI) is a heterogeneous condition with high individual variabilities in clinical outcomes driven by patient demographics, genetics, brain structure features, blood biomarkers, and comorbidities. Multi‐modality data‐driven approaches have been used to discover MCI subtypes; however, disease comorbidities have not been included as a modality though multiple diseases including hypertension are well‐known risk factors for Alzheimer's disease (AD). The aim of this study was to examine MCI heterogeneity in the context of AD‐related comorbidities along with other AD‐relevant features and biomarkers. Methods A total of 325 MCI subjects with 32 AD‐relevant comorbidities and features were considered. Mixed‐data clustering is applied to discover and compare MCI subtypes with and without including AD‐related comorbidities. Finally, the relevance of each comorbidity‐driven subtype was determined by examining their MCI to AD disease prognosis, descriptive statistics, and conversion rates. Results We identified four (five) MCI subtypes: poor‐, average‐, good‐, and best‐AD prognosis by including comorbidities (without including comorbidities). We demonstrated that comorbidity‐driven MCI subtypes differed from those identified without comorbidity information. We further demonstrated the clinical relevance of comorbidity‐driven MCI subtypes. Among the four comorbidity‐driven MCI subtypes there were substantial differences in the proportions of participants who reverted to normal function, remained stable, or converted to AD. The groups showed different behaviors, having significantly different MCI to AD prognosis, significantly different means for cognitive test‐related and plasma features, and by the proportion of comorbidities. Conclusions Our study indicates that AD comorbidities should be considered along with other diverse AD‐relevant characteristics to better understand MCI heterogeneity.

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Molecular Subtyping of Mild Cognitive Impairment Based on Genetic Polymorphism and Gene Expression

Cited by 2 publications

References 28 publications

Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype

Predicting Conversion from MCI to AD Combining Multi-Modality Data and Based on Molecular Subtype

Comorbidity‐driven multi‐modal subtype analysis in mild cognitive impairment of Alzheimer's disease

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