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
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.
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.
Number of individuals
Age, mean ± SE
72.0 ± 1.3
68.6 ± 0.8
Duration of disease (year) c, mean ± SE
4.0 ± 0.5
Disease progressiond early/middle/late stage
MMSE, mean ± SE e
15.2 ± 1.0
MoCA, mean ± SE f
10.9 ± 0.8
Number of individuals
Age, mean ± SE
71.6 ± 1.6
65.5 ± 1.2
62.2 ± 1.8
67.9 ± 2.0
Duration of disease (year), mean ± SE
4.5 ± 0.5
3.9 ± 0.8
Disease progression early/middle/late stage
MMSE, mean ± SE
15.4 ± 1.5
25.1 ± 1.0
MoCA, mean ± SE
10.8 ± 1.5
20.0 ± 1.5
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  score > 1 in PLS-DA model. (H) Metabolites with VIP  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).
AD vs. Ctrl
0.740 ~ 0.941
AD vs. (Ctrl+NDC)
0.733 ~ 0.928
MCI vs. Ctrl
0.604 ~ 0.928
MCI vs. (Ctrl+NDC)
0.591 ~ 0.900
MCI vs. Ctrl
0.585 ~ 0.932
MCI vs. (Ctrl+NDC)
0.612 ~ 0.928
MCI vs. Ctrl
0.721 ~ 0.979
MCI vs. (Ctrl+NDC)
0.705 ~ 0.973
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.
MahmoudianDehkordi S, Arnold M, Nho K, Ahmad S, Jia W, Xie G, et al. (2019). Altered bile acid profile associates with cognitive impairment in Alzheimer's disease-An emerging role for gut microbiome. Alzheimers Dement, 15:76-92.
Cristofano A, Sapere N, La Marca G, Angiolillo A, Vitale M, Corbi G, et al. (2016). Serum Levels of Acyl-Carnitines along the Continuum from Normal to Alzheimer's Dementia. PLoS One, 11:e0155694.
Reitz C, Mayeux R (2014). Alzheimer disease: epidemiology, diagnostic criteria, risk factors and biomarkers. Biochem Pharmacol, 88:640-651.
Xu XH, Huang Y, Wang G, Chen SD (2012). Metabolomics: a novel approach to identify potential diagnostic biomarkers and pathogenesis in Alzheimer's disease. Neurosci Bull, 28:641-648.
Bayer AJ (2018). The role of biomarkers and imaging in the clinical diagnosis of dementia. Age Ageing, 47:641-643.
Morgan AR, Touchard S, Leckey C, O'Hagan C, Nevado-Holgado AJ, Barkhof F, et al. (2019). Inflammatory biomarkers in Alzheimer's disease plasma. Alzheimers Dement, 15:776-787.
Lin CN, Huang CC, Huang KL, Lin KJ, Yen TC, Kuo HC (2019). A metabolomic approach to identifying biomarkers in blood of Alzheimer's disease. Ann Clin Transl Neurol, 6:537-545.
Startin CM, Ashton NJ, Hamburg S, Hithersay R, Wiseman FK, Mok KY, et al. (2019). Plasma biomarkers for amyloid, tau, and cytokines in Down syndrome and sporadic Alzheimer's disease. Alzheimers Res Ther, 11:26.
Nabers A, Hafermann H, Wiltfang J, Gerwert K (2019). Abeta and tau structure-based biomarkers for a blood- and CSF-based two-step recruitment strategy to identify patients with dementia due to Alzheimer's disease. Alzheimers Dement (Amst), 11:257-263.
Patra K, Soosaipillai A, Sando SB, Lauridsen C, Berge G, Møller I, et al. (2018). Assessment of kallikrein 6 as a cross-sectional and longitudinal biomarker for Alzheimer’s disease. Alzheimers Res Ther, 10:9.
Balietti M, Giuli C, Conti F (2018). Peripheral Blood Brain-Derived Neurotrophic Factor as a Biomarker of Alzheimer's Disease: Are There Methodological Biases? Mol Neurobiol, 55:6661-6672.
Tynkkynen J, Chouraki V, van der Lee SJ, Hernesniemi J, Yang Q, Li S, et al. (2018). Association of branched-chain amino acids and other circulating metabolites with risk of incident dementia and Alzheimer's disease: A prospective study in eight cohorts. Alzheimers Dement, 14:723-733.
Snowden SG, Ebshiana AA, Hye A, An Y, Pletnikova O, O'Brien R, et al. (2017). Association between fatty acid metabolism in the brain and Alzheimer disease neuropathology and cognitive performance: A nontargeted metabolomic study. PLoS Med, 14:e1002266.
Bergin DH, Jing Y, Mockett BG, Zhang H, Abraham WC, Liu P (2018). Altered plasma arginine metabolome precedes behavioural and brain arginine metabolomic profile changes in the APPswe/PS1DeltaE9 mouse model of Alzheimer's disease. Transl Psychiatry, 8:108.
Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, et al. (2014). Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med, 20:415-418.
Chouraki V, Preis SR, Yang Q, Beiser A, Li S, Larson MG, et al. (2017). Association of amine biomarkers with incident dementia and Alzheimer's disease in the Framingham Study. Alzheimers Dement, 13:1327-1336.
Casanova R, Varma S, Simpson B, Kim M, An Y, Saldana S, et al. (2016). Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals. Alzheimers Dement, 12:815-822.
Mckhann G, Drachman D, Folstein MF, Katzman R, Price D, Stadlan E (1984). Clinical diagnosis of Alzheimer disease: report of NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Service Task Force on Alzheimer's Disease. Neurology, 34:939-944.
Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E (1999). Mild Cognitive Impairment: Clinical Characterization and Outcome. Arch Neurol, 56:303-308.
Petersen RC (2004). Mild cognitive impairment as a diagnostic entity. J Intern Med, 256:183-194.
Huang Y, Chen G, Liu X, Shao Y, Gao P, Xin C, et al. (2014). Serum metabolomics study and eicosanoid analysis of childhood atopic dermatitis based on liquid chromatography-mass spectrometry. J Proteome Res, 13:5715-5723.
Luo P, Yin P, Hua R, Tan Y, Li Z, Qiu G, et al. (2018). A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma. Hepatology, 67:662-675.
Ren S, Shao Y, Zhao X, Hong CS, Wang F, Lu X, et al. (2016). Integration of Metabolomics and Transcriptomics Reveals Major Metabolic Pathways and Potential Biomarker Involved in Prostate Cancer. Mol Cell Proteomics, 15:154-163.
Zhao X, Zeng Z, Chen A, Lu X, Zhao C, Hu C, et al. (2018). Comprehensive Strategy to Construct In-House Database for Accurate and Batch Identification of Small Molecular Metabolites. Anal Chem, 90:7635-7643.
Smilde AK, van der Werf MJ, Bijlsma S, van der Werff-van der VatBJC, JellemaRH (2005). Fusion of mass spectrometry-based metabolomics data. Anal Chem, 77:6729-6736.
Cunnane SC, Schneider JA, Tangney C, Tremblay-Mercier J, Fortier M, Bennett DA, et al. (2012). Plasma and brain fatty acid profiles in mild cognitive impairment and Alzheimer's disease. J Alzheimers Dis, 29:691-697.
Astarita G, Jung KM, Berchtold NC, Nguyen VQ, Gillen DL, Head E, et al. (2010). Deficient liver biosynthesis of docosahexaenoic acid correlates with cognitive impairment in Alzheimer's disease. PLoS One, 5:e12538.
Thomas MH, Paris C, Magnien M, Colin J, Pelleieux S, Coste F, et al. (2017). Dietary arachidonic acid increases deleterious effects of amyloid-beta oligomers on learning abilities and expression of AMPA receptors: putative role of the ACSL4-cPLA2 balance. Alzheimers Res Ther, 9:69.
Swanson D, Block R, Mousa SA (2012). Omega-3 fatty acids EPA and DHA: health benefits throughout life. Adv Nutr, 3:1-7.
Quinn JF, Raman R, Thomas RG, Yurko-Mauro K, Nelson EB, Van Dyck C, et al. (2010). Docosahexaenoic acid supplementation and cognitive decline in Alzheimer disease: a randomized trial. JAMA, 304:1903-1911.
Amtul Z, Uhrig M, Wang L, Rozmahel RF, Beyreuther K (2012). Detrimental effects of arachidonic acid and its metabolites in cellular and mouse models of Alzheimer's disease: structural insight. Neurobiol Aging, 33:831.e821-831.e831.
Stempler S, Yizhak K, Ruppin E (2014). Integrating transcriptomics with metabolic modeling predicts biomarkers and drug targets for Alzheimer's disease. PLoS One, 9:e105383.
Dey KK, Wang H, Niu M, Bai B, Wang X, Li Y, et al. (2019). Deep undepleted human serum proteome profiling toward biomarker discovery for Alzheimer's disease. Clin Proteomics, 16:16.
Raúl GD, Tamara GB, Javier V, José Luis GA (2015). Metabolomic screening of regional brain alterations in the APP/PS1 transgenic model of Alzheimer's disease by direct infusion mass spectrometry. J Pharm Biomed Anal, 102:425-435.
Xie G, Wang X, Huang F, Zhao A, Chen W, Yan J, et al. (2016). Dysregulated hepatic bile acids collaboratively promote liver carcinogenesis. Int J Cancer, 139:1764-1775.
Nho K, Kueider-Paisley A, MahmoudianDehkordi S, Arnold M, Risacher SL, Louie G, et al. (2019). Altered bile acid profile in mild cognitive impairment and Alzheimer's disease: Relationship to neuroimaging and CSF biomarkers. Alzheimers Dement, 15:232-244.
Pan X, Elliott CT, McGuinness B, Passmore P, Kehoe PG, Hölscher C, et al. (2017). Metabolomic Profiling of Bile Acids in Clinical and Experimental Samples of Alzheimer’s Disease. Metabolites, 7:28.
Adachi Y, Shimodaira Y, Nakamura H, Imaizumi A, Mori M, Kageyama Y, et al. (2017). Low plasma tryptophan is associated with olfactory function in healthy elderly community dwellers in Japan. BMC Geriatr, 17:239.
Hornedo-Ortega R, Da Costa G, Cerezo AB, Troncoso AM, Richard T, Garcia-Parrilla MC (2018). In Vitro Effects of Serotonin, Melatonin, and Other Related Indole Compounds on Amyloid-β Kinetics and Neuroprotection. Mol Nutr Food Res, 62:1700383.
Marksteiner J, Blasko I, Kemmler G, Koal T, Humpel C (2018). Bile acid quantification of 20 plasma metabolites identifies lithocholic acid as a putative biomarker in Alzheimer's disease. Metabolomics, 14:1.
Nir Barzilai, James C Appleby, Steven N Austad, Ana Maria Cuervo, Matt Kaeberlein, Christian Gonzalez-Billault, Stephanie Lederman, Ilia Stambler, Felipe Sierra. Geroscience in the Age of COVID-19[J]. Aging and disease, 2020, 11(4): 725-729.