Dataset Information

Discovering new peripheral plasma biomarkers to identify cognitive decline in type 2 diabetes

ABSTRACT: Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD), thus, identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl, n=30), and the age-/sex-matched T2DM patients with mild cognitive impairment (T2DM-MCI, n=30) or T2DM without MCI (T2DM-nMCI, n=25). The candidate biomarkers identified by proteomics and bioinformatics analyses were verified by ELISA, and their diagnostic capabilities were evaluated with machine learning. A total of 53 differentially expressed proteins (DEPs) were identified in T2DM-MCI compared with T2DM-nMCI patients. These DEPs were significantly enriched in multiple biological processes, such as amyloid neuropathies, CNS disorders, and metabolic acidosis. Among the DEPs, alpha-1-antitrypsin (SERPINA1), major viral protein (PRNP), valosin-containing protein (VCP) showed strong collection with AD high-risk genes APP, MAPT, APOE, PSEN1, and PSEN2. And the levels of PP2A cancer inhibitor (CIP2A), PRNP, corticotropin releasing factor binding protein (CRHBP) were significantly increased, while the level of VCP was decreased in T2DM-MCI patients compared with the T2DM-nMCI, and these changes were correlated with the mini-mental state examination (MMSE) score. Further machine learning data showed that increases of CIP2A, ratio of β-amyloid (rAβ42/40), and rGSK-3β(T/S9) (ratio of total to serine-9-phosphorylated glycogen synthase kinase-3β) had the greatest power to identify mild cognitive decline in T2DM patients.

INSTRUMENT(S): Q Exactive HF

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood Cell, Blood Plasma

DISEASE(S): Type 2 Diabetes Mellitus

SUBMITTER: haitao Yu

LAB HEAD: Jian-Zhi Wang

PROVIDER: PXD027870 | Pride | 2023-03-11

REPOSITORIES: Pride

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Discovering new peripheral plasma biomarkers to identify cognitive decline in type 2 diabetes.

Yu Haitao H Gao Yang Y He Ting T Li Mengzhu M Zhang Yao Y Zheng Jie J Jiang Bijun B Chen Chongyang C Ke Dan D Liu Yanchao Y Wang Jian-Zhi JZ

Frontiers in cell and developmental biology 20221124

Type 2 diabetes mellitus (T2DM) is an independent risk factor of Alzheimer's disease (AD), and thus identifying who among the increasing T2DM populations may develop into AD is important for early intervention. By using TMT-labeling coupled high-throughput mass spectrometry, we conducted a comprehensive plasma proteomic analysis in none-T2DM people (Ctrl, n = 30), and the age-/sex-matched T2DM patients with mild cognitive impairment (T2DM-MCI, n = 30) or T2DM without MCI (T2DM-nMCI ...[more]

PMID: 36506101

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Project description:<h4>INTRODUCTION Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.</h4>METHODS We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.RESULTS 57 AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.DISCUSSION In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.

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