High-resolution NMR metabolomics of patients with subjective cognitive decline plus: Perturbations in the metabolism of glucose and branched-chain amino acids

Yang, Zhongxian; Wang, Jinhua; Chen, Jin; Luo, Min; Xie, Qiuling; Yu, Rong; Wu, Yi Hui; Cao, Zhen; Liu, Yubao

doi:10.1016/j.nbd.2022.105782

Cited by 6 publications

(2 citation statements)

References 79 publications

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“…The significantly enriched sugar metabolism module (Fig. 5) supports the recent perspective that asymptomatic and symptomatic AD is characterized by dysregulation of energy metabolism [33,34]. In short, the presented work supports the hypothesis that sugar metabolism becomes more impacted with disease progression.…”

Section: Sugar Metabolism Biomarkers Enriched In 29-protein and 88-pr...supporting

confidence: 84%

“…Some researchers have referred to the dysregulation of blood sugar in the brain in AD "type 3 diabetes" [36]. Interconnections between inflammation, metabolism, and protein clearance are further evidence of a multifactorial homeostatic instability contributing to AD progression [3,10,34,37].…”

Section: Sugar Metabolism Biomarkers Enriched In 29-protein and 88-pr...mentioning

confidence: 99%

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Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer’s Disease

Tandon

Levey

Lah

et al. 2023

JAD

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Background: The complex and not yet fully understood etiology of Alzheimer’s disease (AD) shows important proteopathic signs which are unlikely to be linked to a single protein. However, protein subsets from deep proteomic datasets can be useful in stratifying patient risk, identifying stage dependent disease markers, and suggesting possible disease mechanisms. Objective: The objective was to identify protein subsets that best classify subjects into control, asymptomatic Alzheimer’s disease (AsymAD), and AD. Methods: Data comprised 6 cohorts; 620 subjects; 3,334 proteins. Brain tissue-derived predictive protein subsets for classifying AD, AsymAD, or control were identified and validated with label-free quantification and machine learning. Results: A 29-protein subset accurately classified AD (AUC = 0.94). However, an 88-protein subset best predicted AsymAD (AUC = 0.85) or Control (AUC = 0.89) from AD (AUC = 0.96). AD versus Control: APP, DHX15, NRXN1, PBXIP1, RABEP1, STOM, and VGF. AD versus AsymAD: ALDH1A1, BDH2, C4A, FABP7, GABBR2, GNAI3, PBXIP1, and PKAR1B. AsymAD versus Control: APP, C4A, DMXL1, EXOC2, PITPNB, REBEP1, and VGF. Additional predictors: DNAJA3, PTBP2, SLC30A9, VAT1L, CROCC, PNP, SNCB, PRKAR1B, ENPP6, HAPLN2, PSMD4, and CMAS. Conclusion: Biomarkers were dynamically separable across disease stages. Predictive proteins were significantly enriched to sugar metabolism.

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Section: Sugar Metabolism Biomarkers Enriched In 29-protein and 88-pr...supporting

confidence: 84%

Section: Sugar Metabolism Biomarkers Enriched In 29-protein and 88-pr...mentioning

confidence: 99%

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer’s Disease

Tandon

Levey

Lah

et al. 2023

JAD

View full text Add to dashboard Cite

show abstract

Integrative metabolomics science in Alzheimer’s disease: Relevance and future perspectives

Lista

González‐Domínguez

López-Ortiz

et al. 2023

Ageing Research Reviews

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In FUS[1−359]‐tg mice O,S-dibenzoyl thiamine reduces muscle atrophy, decreases glycogen synthase kinase 3 beta, and normalizes the metabolome

Probert¹,

Gorlova²,

Deikin³

et al. 2022

Biomedicine & Pharmacotherapy

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High-resolution NMR metabolomics of patients with subjective cognitive decline plus: Perturbations in the metabolism of glucose and branched-chain amino acids

Cited by 6 publications

References 79 publications

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer’s Disease

Machine Learning Selection of Most Predictive Brain Proteins Suggests Role of Sugar Metabolism in Alzheimer’s Disease

Integrative metabolomics science in Alzheimer’s disease: Relevance and future perspectives

In FUS[1−359]‐tg mice O,S-dibenzoyl thiamine reduces muscle atrophy, decreases glycogen synthase kinase 3 beta, and normalizes the metabolome

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