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Aging and disease    2020, Vol. 11 Issue (6) : 1459-1470     DOI: 10.14336/AD.2020.0217
Orginal Article |
Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer’s Disease
Yaping Shao1,2, Yang Ouyang3,4, Tianbai Li1,2, Xinyao Liu1,2, Xiaojiao Xu1,2, Song Li1,2, Guowang Xu3,*, Weidong Le1,2,*
1Center for Clinical Research on Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
2Liaoning Provincial Key Laboratory for Research on the Pathogenic Mechanisms of Neurological Diseases, The First Affiliated Hospital, Dalian Medical University, Dalian, China.
3CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, China.
4University of Chinese Academy of Sciences, Beijing, China
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Abstract  

The expending of elderly population worldwide has resulted in a dramatic rise in the incidence of chronic diseases such as Alzheimer’s disease (AD). Inadequate understanding of the mechanisms underlying AD has hampered the development of efficient tools for definitive diagnosis and curative interventions. Previous studies have attempted to discover reliable biomarkers of AD, but these biomarkers can only be measured through invasive (neuropathological markers in cerebrospinal fluid) or expensive (positron emission tomography scanning or magnetic resonance imaging) techniques. Metabolomics is a high-throughput technology that can detect and catalog large numbers of small metabolites and may be a useful tool for characterization of AD and identification of biomarkers. In this study, we used ultra-performance liquid chromatography-mass spectrometry based untargeted metabolomics to measure the concentrations of plasma metabolites in a cohort of subjects with AD (n=44) and cognitively normal controls (Ctrl, n=94). The AD group showed marked reductions in levels of polyunsaturated fatty acids, acyl-carnitines, degradation products of tryptophan, and elevated levels of bile acids compared to the Ctrl group. We then validated the results using an independent cohort that included subjects with AD (n=30), mild cognitive impairment (MCI, n=13), healthy controls (n=43), and non-AD neurological disease controls (NDC, n=31). We identified five metabolites comprising cholic acid, chenodeoxycholic acid, allocholic acid, indolelactic acid, and tryptophan that were able to distinguish patients with AD from both Ctrl and NDC with satisfactory sensitivity and specificity. The concentrations of these metabolites were significantly correlated with disease severity. Our results also suggested that altered bile acid profiles in AD and MCI might indicate early risk for the development of AD. These findings may allow for development of new approaches for diagnosis of AD and may provide novel insights into AD pathogenesis.

Keywords Alzheimer’s disease      metabolomics      biomarker      metabolic pathway alteration      plasma     
Corresponding Authors: Xu Guowang,Le Weidong   
About author:

these authors contributed equally to this work.

Just Accepted Date: 17 February 2020   Issue Date: 19 November 2020
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Shao Yaping
Ouyang Yang
Li Tianbai
Liu Xinyao
Xu Xiaojiao
Li Song
Xu Guowang
Le Weidong
Cite this article:   
Shao Yaping,Ouyang Yang,Li Tianbai, et al. Alteration of Metabolic Profile and Potential Biomarkers in the Plasma of Alzheimer’s Disease[J]. Aging and disease, 2020, 11(6): 1459-1470.
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http://www.aginganddisease.org/EN/10.14336/AD.2020.0217     OR
Figure 1.  Experimental design. An overview workflow of the metabolomics analysis in Alzheimer's disease. A total of 255 plasma samples were collected and subjected to untargeted metabolomics analysis. Amony them, 138 and 117 plasma samples were included in discovery phase and validation phase, respectively.
AD
(n=74)
Ctrl
(n=137)
NDC (n=31)MCI
(n=13)
p
Cohort 1
Number of individuals4494---
Age, mean ± SE72.0 ± 1.368.6 ± 0.8--0.0156 a
Gender (m/f)20/2451/43--0.3350 b
Duration of disease (year) c, mean ± SE4.0 ± 0.5----
Disease progressiond
early/middle/late stage
17/10/3----
MMSE, mean ± SE e15.2 ± 1.0----
MoCA, mean ± SE f10.9 ± 0.8----
Cohort 2
Number of individuals30433113-
Age, mean ± SE71.6 ± 1.665.5 ± 1.262.2 ± 1.867.9 ± 2.00.0011
Gender (m/f)10/2025/1817/148/50.1459
Duration of disease (year), mean ± SE4.5 ± 0.5--3.9 ± 0.80.5852 g
Disease progression
early/middle/late stage
14/9/4----
MMSE, mean ± SE15.4 ± 1.5--25.1 ± 1.0<0.0001
MoCA, mean ± SE10.8 ± 1.520.0 ± 1.50.0003
Table 1  Clinical information of the subjects in the study.
Figure 2.  Quality control of the analytical method and multivariate statistical analysis. (A) RSD distribution of all the detected features in the QC samples. (B) RSD distribution of the identified metabolites in the QC samples. (C) PCA score plot. QC samples clustered tightly on the plot, indicating good quality control of the analytical method. (D) Standard deviation of the samples. Most of the samples were located within ±2SD. (E) Score plot of the PLS-DA model. Model parameters: R2Y=0.575, Q2=0.427. (F) Permutation test of the PLS-DA model. 999 permutations resulted in intercepts of R2 = 0.266, Q2 = -0.175, indicating an acceptable model without overfitting. (G) Metabolites with VIP [1] score > 1 in PLS-DA model. (H) Metabolites with VIP [2] score > 1 in PLS-DA model.
Figure 3.  Differential metabolites and perturbed metabolic pathways in AD. (A) Heat map of the significantly changed metabolites in AD compared with Ctrl. (B) Pathway enrichment analysis based on differential metabolites.
Figure 4.  Metabolite panel and ROC analysis. (A) Heat map of FAs and acyl-carnitines in Ctrl, NDC and AD. (B) ~ (F) Bar graphs of the 5 metabolites in different groups. The p values were adjusted for multiple testing using Benjamini-Hochberg method. (G) ~ (V) Correlation analysis of the five metabolites with age in AD, Ctrl, NDC and MCI. (W) ~ (X) ROC analysis of the metabolite panels to discriminate AD from Ctrl/(Ctrl +NDC). (Y) ~ (Z) ROC analysis of bile acids to discriminate MCI from Ctrl/(Ctrl +NDC).
Metabolite panelAUC95% CISensitivitySpecificitySESignificance
Meta+ageAD vs. Ctrl0.8400.740 ~ 0.94176.7%83.3%0.05128.57E-07
AD vs. (Ctrl+NDC)0.8310.733 ~ 0.92886.5%70.0%0.04961.39E-07
Allocholic acidMCI vs. Ctrl0.7660.604 ~ 0.92869.2%83.7%0.08273.95E-03
MCI vs. (Ctrl+NDC)0.7450.591 ~ 0.90069.2%77.0%0.07874.96E-03
CAMCI vs. Ctrl0.7580.585 ~ 0.93261.5%88.4%0.08875.04E-03
MCI vs. (Ctrl+NDC)0.7700.612 ~ 0.92861.5%87.8%0.08071.96E-03
CDCAMCI vs. Ctrl0.8500.721 ~ 0.97976.9%90.7%0.06591.48E-04
MCI vs. (Ctrl+NDC)0.8390.705 ~ 0.97376.9%89.2%0.06841.04E-04
Table 2  Results for assessment of plasma metabolite panel in the discrimination of AD/MCI.
Figure 5.  Correlation analysis of the metabolites panel with gender, disease severity and clinical characteristics. (A) ~ (E) Distribution of the levels of metabolite in different genders in Ctrl, NDC, MCI and AD. (F) ~ (K) Correlation analysis of the metabolite levels to disease progress. Correlation coefficients were based on Spearman correlation analysis. #, 0.01 < p < 0.05, ##, 0.001 < p <0.01, ###, 0.0001 < p < 0.001. (L) Correlations analysis of metabolite levels with clinical parameters, which was based on Pearson correlation analysis. Yellow square indicated the correlations between two variables were statistically significant.
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