| BackgroundAlzheimer’s Disease(AD)is a neurodegenerative disorder characterized primarily by progressive cognitive decline and stands as the most common form of dementia.With the global aging population,AD has gradually become the third leading cause of death and disability among the elderly,imposing a significant burden on society and families.While extensive research has been conducted on the mechanisms of AD,the recognized manifestation primarily involves brain structural damage.Studies have confirmed a significant correlation between the reduction in gray matter volume(GMV)in cognitive-related brain regions such as the hippocampus and medial temporal lobe and cognitive impairment in AD.However,AD often has a subtle onset,making it challenging to identify early structural and cognitive changes in clinical practice.This can lead to delays in the early diagnosis of the disease.Currently,β-amyloid protein and phosphorylated tau protein(p-tau)are considered pathological biomarkers of AD,and they can undergo changes before the onset of clinical symptoms in early AD.Although their potential in early diagnosis is widely recognized,there is still a need for further research into other convenient,cost-effective,and non-invasive blood-based biomarkers to assist in the early diagnosis and differentiation of AD.Metabolomics,involving the qualitative and quantitative analysis of small-molecule metabolites in biological samples,has emerged as a crucial tool for studying abnormal metabolism in diseases.Identifying characteristic metabolites provides an important avenue for exploring new diagnostic biomarkers or therapeutic targets in clinical applications and also offers insights into the relationship between metabolites and the physiological and pathological changes of diseases,as well as clinical phenotypes.The primary objective of this study is to investigate changes in plasma metabolites of AD patients and their relationship with brain structural damage.Objectives(1)Study 1: To compare the plasma metabolite differences between AD patients and cognitively unimpaired(CU)participants,explore the characteristic changes in plasma metabolism in AD,identify potential biomarkers,and validate their reliability.(2)Study 2: Among AD patients,investigate the relationship between plasma differential metabolites and brain GMV to gain deeper insights into the possible associations between plasma metabolite disturbances and the occurrence and progression of AD.(3)Study 3: Further explore changes in hippocampal subfield volumes in AD patients and the relationship between these hippocampal subfields and memory function.Investigate the correlation between memory-related hippocampal subfield volumes and plasma differential metabolites in AD patients.Methods(1)Study 1: Non-targeted metabolomics analysis of fasting plasma samples from AD patients and CU participants will be conducted using high-performance liquid chromatography-mass spectrometry technology.Statistical and bioinformatics methods will be employed to identify plasma differential metabolites between AD patients and CU participants.Further pairwise correlation analysis of differential metabolites between the AD and CU groups will be performed.Functional annotation of these differential metabolites and their metabolic pathways will be carried out using databases.For significantly enriched plasma differential metabolites,the area under the curve(AUC)of the Receiver Operating Characteristic curve(ROC)will be calculated,and their relationship with plasma p-tau and cognitive function will be analyzed to confirm their reliability.(2)Study 2: Differences in brain GMV between the AD and CU groups will be compared.Subsequently,correlation analysis will be conducted between plasma differential metabolites and differential brain regions’ GMV in AD patients.Linear regression models will be used,incorporating age,gender,education level,Apolipoprotein E(APOE)status,and total intracranial volume(TIV)as control variables to further assess the degree of association.A binary logistic regression model will be developed step by step to predict the probability of different markers’ combined associations,and ROC curves will be used to calculate the AUC of different regression models.(3)Study 3: Building upon the previous studies,hippocampal subfield volumes will be computed.Group differences in hippocampal subfield volumes between the CU and AD groups will be assessed using a general linear model.Disease grouping will be the independent variable,hippocampal subfield volume as the dependent variable,and age,gender,education level,APOE status,and TIV as covariates,with P-values subjected to FDR correction.Finally,age,gender,education level,APOE status,and TIV will be added as covariates,and partial correlation analysis will be employed to explore the relationship between hippocampal subfield volumes and memory scores,as well as the association between plasma differential metabolites and memory-related hippocampal subfield volumes.Results(1)Study 1: This study included a total of 42 AD patients and 59 CU participants.In both positive and negative ion modes,49 plasma differential metabolites were identified and screened between the two groups.Enrichment analysis indicated enrichment of the biosynthesis pathways of phenylalanine,tyrosine,and tryptophan between the two groups.In this study,L-phenylalanine,indole,and L-tryptophan were enriched in this pathway,with AUC values ranging from 0.73 to 0.75.All three plasma metabolites were significantly correlated with plasma p-tau,with L-phenylalanine and L-tryptophan showing significant correlations with some cognitive functions.(2)Study 2: AD patients exhibited significant brain GMV atrophy in the right hippocampus,right middle temporal gyrus,left superior frontal gyrus,left inferior temporal gyrus,left pallidum,and left middle temporal gyrus.After controlling for variables,differential metabolites significantly associated with right hippocampal GMV included L-glutamate,prostaglandin E1,D-proline,L-cysteine,10-undecenoic acid,5-hydroxyomeprazole,and omeprazole sulfone.ROC analysis revealed that the highest diagnostic accuracy for AD disease was achieved with an AUC of 0.912 when combining APOE gene carrier status,plasma p-tau levels,L-phenylalanine,L-tryptophan,indole,right hippocampal GMV,right middle temporal gyrus GMV,and left superior frontal gyrus GMV.(3)Study 3: AD patients exhibited significantly lower hippocampal subfield volumes compared to the CU group.Among these,the right hippocampal subfields significantly correlated with memory scores included the subiculum,CA1,CA2/3,CA4,dentate gyrus granule cell and molecular layers,hippocampal-molecular layer,hippocampal tail,and hippocampal-amygdala transition area.There were numerous plasma differential metabolites associated with the hippocampal CA2/3 and dentate gyrus granule cell and molecular layers.ConclusionsAD patients exhibit pathological physiological processes such as amino acid lipid metabolism,iron death,and inflammation oxidative stress abnormalities.The biosynthesis pathways of phenylalanine,tyrosine,and tryptophan may play a role in the occurrence and progression of AD.Plasma L-phenylalanine,L-tryptophan,and indole may serve as potential biomarkers for diagnosing AD.There are significant correlations between various plasma metabolites in AD patients and GMV in different brain regions,with some plasma metabolites showing significant correlations with memory-related hippocampal subfield volumes.This may provide new insights for the diagnosis and treatment of AD. |
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