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Chen T, Wang L, Xie G, Kristal BS, Zheng X, Sun T, Arnold M, Louie G, Li M, Wu L, Mahmoudiandehkordi S, Sniatynski MJ, Borkowski K, Guo Q, Kuang J, Wang J, Nho K, Ren Z, Kueider‐Paisley A, Blach C, Kaddurah‐Daouk R, Jia W. Serum Bile Acids Improve Prediction of Alzheimer's Progression in a Sex-Dependent Manner. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2306576. [PMID: 38093507 PMCID: PMC10916590 DOI: 10.1002/advs.202306576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Revised: 11/01/2023] [Indexed: 03/07/2024]
Abstract
Sex disparities in serum bile acid (BA) levels and Alzheimer's disease (AD) prevalence have been established. However, the precise link between changes in serum BAs and AD development remains elusive. Here, authors quantitatively determined 33 serum BAs and 58 BA features in 4 219 samples collected from 1 180 participants from the Alzheimer's Disease Neuroimaging Initiative. The findings revealed that these BA features exhibited significant correlations with clinical stages, encompassing cognitively normal (CN), early and late mild cognitive impairment, and AD, as well as cognitive performance. Importantly, these associations are more pronounced in men than women. Among participants with progressive disease stages (n = 660), BAs underwent early changes in men, occurring before AD. By incorporating BA features into diagnostic and predictive models, positive enhancements are achieved for all models. The area under the receiver operating characteristic curve improved from 0.78 to 0.91 for men and from 0.76 to 0.83 for women for the differentiation of CN and AD. Additionally, the key findings are validated in a subset of participants (n = 578) with cerebrospinal fluid amyloid-beta and tau levels. These findings underscore the role of BAs in AD progression, offering potential improvements in the accuracy of AD prediction.
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Affiliation(s)
- Tianlu Chen
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Lu Wang
- School of Chinese MedicineHong Kong Baptist UniversityKowloon TongHong Kong999077China
| | | | - Bruce S. Kristal
- Division of Sleep and Circadian DisordersDepartment of MedicineBrigham and Women's HospitalBostonMA02115USA
- Division of Sleep MedicineHarvard Medical SchoolBostonMA02115USA
| | - Xiaojiao Zheng
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Tao Sun
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Matthias Arnold
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNC27710USA
- Institute of Bioinformatics and Systems BiologyHelmholtz Zentrum MünchenGerman Research Center for Environmental Health85764NeuherbergGermany
| | - Gregory Louie
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNC27710USA
| | - Mengci Li
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Lirong Wu
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | | | - Matthew J. Sniatynski
- Division of Sleep and Circadian DisordersDepartment of MedicineBrigham and Women's HospitalBostonMA02115USA
- Division of Sleep MedicineHarvard Medical SchoolBostonMA02115USA
| | - Kamil Borkowski
- West Coast Metabolomics CenterGenome CenterUniversity of California DavisDavisCA95616USA
| | - Qihao Guo
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Junliang Kuang
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Jieyi Wang
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease CenterIndiana University School of MedicineIndianapolisIN46202USA
| | - Zhenxing Ren
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
| | | | - Colette Blach
- Duke Molecular Physiology InstituteDuke UniversityDurhamNC27708USA
| | - Rima Kaddurah‐Daouk
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNC27710USA
- Duke Institute of Brain SciencesDuke UniversityDurhamNC27708USA
- Department of MedicineDuke UniversityDurhamNC27708USA
| | - Wei Jia
- Center for Translational MedicineShanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai200233China
- School of Chinese MedicineHong Kong Baptist UniversityKowloon TongHong Kong999077China
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Gordon S, Lee JS, Scott TM, Bhupathiraju S, Ordovas J, Kelly RS, Bhadelia R, Koo BB, Bigornia S, Tucker KL, Palacios N. Metabolites and MRI-Derived Markers of AD/ADRD Risk in a Puerto Rican Cohort. RESEARCH SQUARE 2024:rs.3.rs-3941791. [PMID: 38410484 PMCID: PMC10896402 DOI: 10.21203/rs.3.rs-3941791/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
Objective Several studies have examined metabolomic profiles in relation to Alzheimer's disease and related dementia (AD/ADRD) risk; however, few studies have focused on minorities, such as Latinos, or examined Magnetic-Resonance Imaging (MRI)-based outcomes. Methods We used multiple linear regression, adjusted for covariates, to examine the association between metabolite concentration and MRI-derived brain age deviation. Metabolites were measured at baseline with untargeted metabolomic profiling (Metabolon, Inc). Brain age deviation (BAD) was calculated at wave 4 (~ 9 years from Boston Puerto Rican Health Study (BPRHS) baseline) as chronologic age, minus MRI-estimated brain age, representing the rate of biological brain aging relative to chronologic age. We also examined if metabolites associated with BAD were similarly associated with hippocampal volume and global cognitive function at wave 4 in the BPRHS. Results Several metabolites, including isobutyrylcarnitine, propionylcarnitine, phenylacetylglutamine, phenylacetylcarnitine (acetylated peptides), p-cresol-glucuronide, phenylacetylglutamate, and trimethylamine N-oxide (TMAO) were inversely associated with brain age deviation. Taurocholate sulfate, a bile salt, was marginally associated with better brain aging. Most metabolites with negative associations with brain age deviation scores also were inversely associations with hippocampal volumes and wave 4 cognitive function. Conclusion The metabolites identifiedin this study are generally consistent with prior literature and highlight the role of BCAA, TMAO and microbially derived metabolites in cognitive decline.
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Zhuang H, Cao X, Tang X, Zou Y, Yang H, Liang Z, Yan X, Chen X, Feng X, Shen L. Investigating metabolic dysregulation in serum of triple transgenic Alzheimer's disease male mice: implications for pathogenesis and potential biomarkers. Amino Acids 2024; 56:10. [PMID: 38315232 PMCID: PMC10844422 DOI: 10.1007/s00726-023-03375-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Accepted: 11/11/2023] [Indexed: 02/07/2024]
Abstract
Alzheimer's disease (AD) is a multifactorial neurodegenerative disease that lacks convenient and accessible peripheral blood diagnostic markers and effective drugs. Metabolic dysfunction is one of AD risk factors, which leaded to alterations of various metabolites in the body. Pathological changes of the brain can be reflected in blood metabolites that are expected to explain the disease mechanisms or be candidate biomarkers. The aim of this study was to investigate the changes of targeted metabolites within peripheral blood of AD mouse model, with the purpose of exploring the disease mechanism and potential biomarkers. Targeted metabolomics was used to quantify 256 metabolites in serum of triple transgenic AD (3 × Tg-AD) male mice. Compared with controls, 49 differential metabolites represented dysregulation in purine, pyrimidine, tryptophan, cysteine and methionine and glycerophospholipid metabolism. Among them, adenosine, serotonin, N-acetyl-5-hydroxytryptamine, and acetylcholine play a key role in regulating neural transmitter network. The alteration of S-adenosine-L-homocysteine, S-adenosine-L-methionine, and trimethylamine-N-oxide in AD mice serum can served as indicator of AD risk. The results revealed the changes of metabolites in serum, suggesting that metabolic dysregulation in periphery in AD mice may be related to the disturbances in neuroinhibition, the serotonergic system, sleep function, the cholinergic system, and the gut microbiota. This study provides novel insights into the dysregulation of several key metabolites and metabolic pathways in AD, presenting potential avenues for future research and the development of peripheral biomarkers.
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Affiliation(s)
- Hongbin Zhuang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xueshan Cao
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xiaoxiao Tang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Yongdong Zou
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Hongbo Yang
- Center for Instrumental Analysis, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Zhiyuan Liang
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Xi Yan
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xiaolu Chen
- The Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, School of Public Health, Guizhou Medical University, Guiyang, 550025, People's Republic of China
| | - Xingui Feng
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China
| | - Liming Shen
- College of Life Science and Oceanography, Shenzhen University, Shenzhen, 518071, People's Republic of China.
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, People's Republic of China.
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Veitch DP, Weiner MW, Miller M, Aisen PS, Ashford MA, Beckett LA, Green RC, Harvey D, Jack CR, Jagust W, Landau SM, Morris JC, Nho KT, Nosheny R, Okonkwo O, Perrin RJ, Petersen RC, Rivera Mindt M, Saykin A, Shaw LM, Toga AW, Tosun D. The Alzheimer's Disease Neuroimaging Initiative in the era of Alzheimer's disease treatment: A review of ADNI studies from 2021 to 2022. Alzheimers Dement 2024; 20:652-694. [PMID: 37698424 PMCID: PMC10841343 DOI: 10.1002/alz.13449] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 07/27/2023] [Accepted: 08/01/2023] [Indexed: 09/13/2023]
Abstract
The Alzheimer's Disease Neuroimaging Initiative (ADNI) aims to improve Alzheimer's disease (AD) clinical trials. Since 2006, ADNI has shared clinical, neuroimaging, and cognitive data, and biofluid samples. We used conventional search methods to identify 1459 publications from 2021 to 2022 using ADNI data/samples and reviewed 291 impactful studies. This review details how ADNI studies improved disease progression understanding and clinical trial efficiency. Advances in subject selection, detection of treatment effects, harmonization, and modeling improved clinical trials and plasma biomarkers like phosphorylated tau showed promise for clinical use. Biomarkers of amyloid beta, tau, neurodegeneration, inflammation, and others were prognostic with individualized prediction algorithms available online. Studies supported the amyloid cascade, emphasized the importance of neuroinflammation, and detailed widespread heterogeneity in disease, linked to genetic and vascular risk, co-pathologies, sex, and resilience. Biological subtypes were consistently observed. Generalizability of ADNI results is limited by lack of cohort diversity, an issue ADNI-4 aims to address by enrolling a diverse cohort.
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Affiliation(s)
- Dallas P. Veitch
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Michael W. Weiner
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of MedicineUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
- Department of NeurologyUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Melanie Miller
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
| | - Paul S. Aisen
- Alzheimer's Therapeutic Research InstituteUniversity of Southern CaliforniaSan DiegoCaliforniaUSA
| | - Miriam A. Ashford
- Department of Veterans Affairs Medical CenterNorthern California Institute for Research and Education (NCIRE)San FranciscoCaliforniaUSA
| | - Laurel A. Beckett
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | - Robert C. Green
- Division of GeneticsDepartment of MedicineBrigham and Women's HospitalBroad Institute Ariadne Labs and Harvard Medical SchoolBostonMassachusettsUSA
| | - Danielle Harvey
- Division of BiostatisticsDepartment of Public Health SciencesUniversity of CaliforniaDavisCaliforniaUSA
| | | | - William Jagust
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - Susan M. Landau
- Helen Wills Neuroscience InstituteUniversity of California BerkeleyBerkeleyCaliforniaUSA
| | - John C. Morris
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | - Kwangsik T. Nho
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Center for Computational Biology and BioinformaticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Rachel Nosheny
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Psychiatry and Behavioral SciencesUniversity of CaliforniaSan FranciscoCaliforniaUSA
| | - Ozioma Okonkwo
- Wisconsin Alzheimer's Disease Research Center and Department of MedicineUniversity of Wisconsin School of Medicine and Public HealthMadisonWisconsinUSA
| | - Richard J. Perrin
- Knight Alzheimer's Disease Research CenterWashington University School of MedicineSaint LouisMissouriUSA
- Department of NeurologyWashington University School of MedicineSaint LouisMissouriUSA
- Department of Pathology and ImmunologyWashington University School of MedicineSaint LouisMissouriUSA
| | | | - Monica Rivera Mindt
- Department of PsychologyLatin American and Latino Studies InstituteAfrican and African American StudiesFordham UniversityNew YorkNew YorkUSA
- Department of NeurologyIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Andrew Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer's Disease Research CenterIndiana University School of MedicineIndianapolisIndianaUSA
- Department of Medical and Molecular GeneticsIndiana University School of MedicineIndianapolisIndianaUSA
| | - Leslie M. Shaw
- Department of Pathology and Laboratory Medicine and the PENN Alzheimer's Disease Research CenterCenter for Neurodegenerative ResearchPerelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Arthur W. Toga
- Laboratory of Neuro ImagingInstitute of Neuroimaging and InformaticsKeck School of Medicine of University of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Duygu Tosun
- Department of Veterans Affairs Medical CenterCenter for Imaging of Neurodegenerative DiseasesSan FranciscoCaliforniaUSA
- Department of Radiology and Biomedical ImagingUniversity of CaliforniaSan FranciscoCaliforniaUSA
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Dilmore AH, Martino C, Neth BJ, West KA, Zemlin J, Rahman G, Panitchpakdi M, Meehan MJ, Weldon KC, Blach C, Schimmel L, Kaddurah-Daouk R, Dorrestein PC, Knight R, Craft S. Effects of a ketogenic and low-fat diet on the human metabolome, microbiome, and foodome in adults at risk for Alzheimer's disease. Alzheimers Dement 2023; 19:4805-4816. [PMID: 37017243 PMCID: PMC10551050 DOI: 10.1002/alz.13007] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 01/17/2023] [Accepted: 01/23/2023] [Indexed: 04/06/2023]
Abstract
INTRODUCTION The ketogenic diet (KD) is an intriguing therapeutic candidate for Alzheimer's disease (AD) given its protective effects against metabolic dysregulation and seizures. Gut microbiota are essential for KD-mediated neuroprotection against seizures as well as modulation of bile acids, which play a major role in cholesterol metabolism. These relationships motivated our analysis of gut microbiota and metabolites related to cognitive status following a controlled KD intervention compared with a low-fat-diet intervention. METHODS Prediabetic adults, either with mild cognitive impairment (MCI) or cognitively normal (CN), were placed on either a low-fat American Heart Association diet or high-fat modified Mediterranean KD (MMKD) for 6 weeks; then, after a 6-week washout period, they crossed over to the alternate diet. We collected stool samples for shotgun metagenomics and untargeted metabolomics at five time points to investigate individuals' microbiome and metabolome throughout the dietary interventions. RESULTS Participants with MCI on the MMKD had lower levels of GABA-producing microbes Alistipes sp. CAG:514 and GABA, and higher levels of GABA-regulating microbes Akkermansia muciniphila. MCI individuals with curcumin in their diet had lower levels of bile salt hydrolase-containing microbes and an altered bile acid pool, suggesting reduced gut motility. DISCUSSION Our results suggest that the MMKD may benefit adults with MCI through modulation of GABA levels and gut-transit time.
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Affiliation(s)
- Amanda Hazel Dilmore
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA
| | - Cameron Martino
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
| | | | - Kiana A. West
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Jasmine Zemlin
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Gibraan Rahman
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA
| | - Morgan Panitchpakdi
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Michael J. Meehan
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Kelly C. Weldon
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Colette Blach
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Leyla Schimmel
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC
- Department of Medicine, Duke University, Durham, NC
- Duke Institute of Brain Sciences, Duke University, Durham, NC
| | - Pieter C Dorrestein
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Collaborative Mass Spectrometry Innovation Center, University of California San Diego, La Jolla, CA
- Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California San Diego, La Jolla, CA
| | - Rob Knight
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Center for Microbiome Innovation, Joan and Irwin Jacobs School of Engineering, University of California San Diego, La Jolla, CA
- Department of Bioengineering, University of California San Diego, La Jolla, CA
- Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA
| | - Suzanne Craft
- Department of Internal Medicine, Section on Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC
| | - Alzheimer’s Gut Microbiome Project Consortium
- Department of Pediatrics, University of California San Diego, La Jolla, CA
- Department of Medicine, Duke University, Durham, NC
- Department of Internal Medicine, Section on Geriatrics and Gerontology, Wake Forest School of Medicine, Winston-Salem, NC
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Jo T, Kim J, Bice P, Huynh K, Wang T, Arnold M, Meikle PJ, Giles C, Kaddurah-Daouk R, Saykin AJ, Nho K. Circular-SWAT for deep learning based diagnostic classification of Alzheimer's disease: application to metabolome data. EBioMedicine 2023; 97:104820. [PMID: 37806288 PMCID: PMC10579282 DOI: 10.1016/j.ebiom.2023.104820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/10/2023] Open
Abstract
BACKGROUND Deep learning has shown potential in various scientific domains but faces challenges when applied to complex, high-dimensional multi-omics data. Alzheimer's Disease (AD) is a neurodegenerative disorder that lacks targeted therapeutic options. This study introduces the Circular-Sliding Window Association Test (c-SWAT) to improve the classification accuracy in predicting AD using serum-based metabolomics data, specifically lipidomics. METHODS The c-SWAT methodology builds upon the existing Sliding Window Association Test (SWAT) and utilizes a three-step approach: feature correlation analysis, feature selection, and classification. Data from 997 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) served as the basis for model training and validation. Feature correlations were analyzed using Weighted Gene Co-expression Network Analysis (WGCNA), and Convolutional Neural Networks (CNN) were employed for feature selection. Random Forest was used for the final classification. FINDINGS The application of c-SWAT resulted in a classification accuracy of up to 80.8% and an AUC of 0.808 for distinguishing AD from cognitively normal older adults. This marks a 9.4% improvement in accuracy and a 0.169 increase in AUC compared to methods without c-SWAT. These results were statistically significant, with a p-value of 1.04 × 10ˆ-4. The approach also identified key lipids associated with AD, such as Cer(d16:1/22:0) and PI(37:6). INTERPRETATION Our results indicate that c-SWAT is effective in improving classification accuracy and in identifying potential lipid biomarkers for AD. These identified lipids offer new avenues for understanding AD and warrant further investigation. FUNDING The specific funding of this article is provided in the acknowledgements section.
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Affiliation(s)
- Taeho Jo
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Junpyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Medical Research Institute, Sungkyunkwan University, School of Medicine, Seoul, South Korea
| | - Paula Bice
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Kevin Huynh
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Tingting Wang
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Matthias Arnold
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, 85764, Germany
| | - Peter J Meikle
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia; Monash University, Melbourne, VIC 3800, Australia
| | - Corey Giles
- Baker Heart and Diabetes Institute, Melbourne, 3004, Victoria, Australia; Baker Department of Cardiometabolic Health, University of Melbourne, Parkville, 3010, Victoria, Australia
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, 27710, USA; Duke Institute of Brain Sciences, Duke University, Durham, NC, 27710, USA; Department of Medicine, Duke University, Durham, NC, 27710, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
| | - Kwangsik Nho
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Indiana Alzheimer Disease Research Center, Indiana University School of Medicine, Indianapolis, IN, 46202, USA; Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.
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7
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Ferretti G, Serafini S, Angiolillo A, Monterosso P, Di Costanzo A, Matrone C. Advances in peripheral blood biomarkers of patients with Alzheimer's disease: Moving closer to personalized therapies. Biomed Pharmacother 2023; 165:115094. [PMID: 37392653 DOI: 10.1016/j.biopha.2023.115094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2023] [Revised: 06/17/2023] [Accepted: 06/27/2023] [Indexed: 07/03/2023] Open
Abstract
Recently, measurable peripheral biomarkers in the plasma of patients with Alzheimer's disease (AD) have gained considerable clinical interest. Several studies have identified one or more blood signatures that may facilitate the development of novel diagnostic and therapeutic strategies. For instance, changes in peripheral amyloid β42 (Aβ42) levels have been largely investigated in patients with AD and correlated with the progression of the pathology, although with controversial results. In addition, tumor necrosis factor α (TNFα) has been identified as an inflammatory biomarker strongly associated with AD, and several studies have consistently suggested the pharmacological targeting of TNFα to reduce systemic inflammation and prevent neurotoxicity in AD. Moreover, alterations in plasma metabolite levels appear to predict the progression of systemic processes relevant to brain functions. In this study, we analyzed the changes in the levels of Aβ42, TNFα, and plasma metabolites in subjects with AD and compared the results with those in healthy elderly (HE) subjects. Differences in plasma metabolites of patients with AD were analyzed with respect to Aβ42, TNFα, and the Mini-Mental State Examination (MMSE) score, searching for plasma signatures that changed simultaneously. In addition, the phosphorylation levels of the Tyr682 residue of the amyloid precursor protein (APP), which we previously proposed as a biomarker of AD, were measured in five HE and five AD patients, in whom the levels of Aβ42, TNFα, and two plasma lipid metabolites increased simultaneously. Overall, this study highlights the potential of combining different plasma signatures to define specific clinical phenotypes of patient subgroups, thus paving the way for the stratification of patients with AD and development of personalized approaches.
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Affiliation(s)
- Gabriella Ferretti
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Sara Serafini
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Antonella Angiolillo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100 Campobasso, Italy
| | - Paola Monterosso
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy
| | - Alfonso Di Costanzo
- Department of Medicine and Health Sciences, Center for Research and Training in Aging Medicine, University of Molise, 86100 Campobasso, Italy
| | - Carmela Matrone
- Unit of Pharmacology, Department of Neuroscience, Faculty of Medicine, University of Naples Federico II, Via Pansini, 5 80131 Naples, Italy.
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8
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Green RE, Lord J, Scelsi MA, Xu J, Wong A, Naomi-James S, Handy A, Gilchrist L, Williams DM, Parker TD, Lane CA, Malone IB, Cash DM, Sudre CH, Coath W, Thomas DL, Keuss S, Dobson R, Legido-Quigley C, Fox NC, Schott JM, Richards M, Proitsi P. Investigating associations between blood metabolites, later life brain imaging measures, and genetic risk for Alzheimer's disease. Alzheimers Res Ther 2023; 15:38. [PMID: 36814324 PMCID: PMC9945600 DOI: 10.1186/s13195-023-01184-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 02/08/2023] [Indexed: 02/24/2023]
Abstract
BACKGROUND Identifying blood-based signatures of brain health and preclinical pathology may offer insights into early disease mechanisms and highlight avenues for intervention. Here, we systematically profiled associations between blood metabolites and whole-brain volume, hippocampal volume, and amyloid-β status among participants of Insight 46-the neuroscience sub-study of the National Survey of Health and Development (NSHD). We additionally explored whether key metabolites were associated with polygenic risk for Alzheimer's disease (AD). METHODS Following quality control, levels of 1019 metabolites-detected with liquid chromatography-mass spectrometry-were available for 1740 participants at age 60-64. Metabolite data were subsequently clustered into modules of co-expressed metabolites using weighted coexpression network analysis. Accompanying MRI and amyloid-PET imaging data were present for 437 participants (age 69-71). Regression analyses tested relationships between metabolite measures-modules and hub metabolites-and imaging outcomes. Hub metabolites were defined as metabolites that were highly connected within significant (pFDR < 0.05) modules or were identified as a hub in a previous analysis on cognitive function in the same cohort. Regression models included adjustments for age, sex, APOE genotype, lipid medication use, childhood cognitive ability, and social factors. Finally, associations were tested between AD polygenic risk scores (PRS), including and excluding the APOE region, and metabolites and modules that significantly associated (pFDR < 0.05) with an imaging outcome (N = 1638). RESULTS In the fully adjusted model, three lipid modules were associated with a brain volume measure (pFDR < 0.05): one enriched in sphingolipids (hippocampal volume: ß = 0.14, 95% CI = [0.055,0.23]), one in several fatty acid pathways (whole-brain volume: ß = - 0.072, 95%CI = [- 0.12, - 0.026]), and another in diacylglycerols and phosphatidylethanolamines (whole-brain volume: ß = - 0.066, 95% CI = [- 0.11, - 0.020]). Twenty-two hub metabolites were associated (pFDR < 0.05) with an imaging outcome (whole-brain volume: 22; hippocampal volume: 4). Some nominal associations were reported for amyloid-β, and with an AD PRS in our genetic analysis, but none survived multiple testing correction. CONCLUSIONS Our findings highlight key metabolites, with functions in membrane integrity and cell signalling, that associated with structural brain measures in later life. Future research should focus on replicating this work and interrogating causality.
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Affiliation(s)
- Rebecca E Green
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK
| | - Jodie Lord
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Marzia A Scelsi
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK
| | - Jin Xu
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,Institute of Pharmaceutical Science, King's College London, London, UK
| | - Andrew Wong
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK
| | - Sarah Naomi-James
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Alex Handy
- University College London, Institute of Health Informatics, London, UK
| | - Lachlan Gilchrist
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK
| | - Dylan M Williams
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Thomas D Parker
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Department of Brain Sciences, Imperial College London, London, W12 0NN, UK.,UK DRI Centre for Care Research and Technology, Imperial College London, London, W12 0BZ, UK
| | - Christopher A Lane
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Ian B Malone
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Carole H Sudre
- Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing (CMIC), University College London (UCL), London, UK.,MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.,Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - William Coath
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - David L Thomas
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Sarah Keuss
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK
| | - Richard Dobson
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.,UK National Institute for Health Research (NIHR) Maudsley Biomedical Research Centre, South London and Maudsley Trust, London, UK.,University College London, Institute of Health Informatics, London, UK.,Health Data Research UK London, University College London, London, UK.,NIHR Biomedical Research Centre at University College London Hospitals NHS Foundation Trust, London, UK
| | - Cristina Legido-Quigley
- Institute of Pharmaceutical Science, King's College London, London, UK.,Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.,UK Dementia Research Institute at University College London, London, UK
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, 8-11 Queen Square, London, WC1N 3BG, UK.
| | - Marcus Richards
- MRC Unit for Lifelong Health & Ageing at UCL, University College London, Floor 5, MRC LHA at UCL, 1 - 19 Torrington Place, London, WC1E 7HB, UK.
| | - Petroula Proitsi
- Institute of Psychiatry, Psychology & Neuroscience, King's College London, 16 De Crespigny Park, London, SE5 8AB, UK.
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9
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Chang R, Trushina E, Zhu K, Zaidi SSA, Lau BM, Kueider-Paisley A, Moein S, He Q, Alamprese ML, Vagnerova B, Tang A, Vijayan R, Liu Y, Saykin AJ, Brinton RD, Kaddurah-Daouk R. Predictive metabolic networks reveal sex- and APOE genotype-specific metabolic signatures and drivers for precision medicine in Alzheimer's disease. Alzheimers Dement 2023; 19:518-531. [PMID: 35481667 PMCID: PMC10402890 DOI: 10.1002/alz.12675] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 01/20/2022] [Accepted: 02/17/2022] [Indexed: 12/19/2022]
Abstract
INTRODUCTION Late-onset Alzheimer's disease (LOAD) is a complex neurodegenerative disease characterized by multiple progressive stages, glucose metabolic dysregulation, Alzheimer's disease (AD) pathology, and inexorable cognitive decline. Discovery of metabolic profiles unique to sex, apolipoprotein E (APOE) genotype, and stage of disease progression could provide critical insights for personalized LOAD medicine. METHODS Sex- and APOE-specific metabolic networks were constructed based on changes in 127 metabolites of 656 serum samples from the Alzheimer's Disease Neuroimaging Initiative cohort. RESULTS Application of an advanced analytical platform identified metabolic drivers and signatures clustered with sex and/or APOE ɛ4, establishing patient-specific biomarkers predictive of disease state that significantly associated with cognitive function. Presence of the APOE ɛ4 shifts metabolic signatures to a phosphatidylcholine-focused profile overriding sex-specific differences in serum metabolites of AD patients. DISCUSSION These findings provide an initial but critical step in developing a diagnostic platform for personalized medicine by integrating metabolomic profiling and cognitive assessments to identify targeted precision therapeutics for AD patient subgroups through computational network modeling.
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Affiliation(s)
- Rui Chang
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Eugenia Trushina
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, MN, USA
| | - Kuixi Zhu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Syed Shujaat Ali Zaidi
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Branden M. Lau
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | | | - Sara Moein
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Qianying He
- Department of Biosystems Engineering, University of Arizona, Tucson, AZ, USA
| | - Melissa L. Alamprese
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
| | - Barbora Vagnerova
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Andrew Tang
- Department of Neuroscience, University of Arizona, Tucson, AZ, USA
| | | | - Yanyun Liu
- Department of Neurology, University of Arizona, Tucson, AZ, USA
| | - Andrew J. Saykin
- Department of Radiology and Imaging Sciences and the Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Roberta D. Brinton
- Department of Neurology, University of Arizona, Tucson, AZ, USA
- The Center for Innovation in Brain Science, University of Arizona, Tucson, AZ, USA
- Department of Pharmacology, College of Medicine, University of Arizona, Tucson, AZ, USA
| | - Rima Kaddurah-Daouk
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA
- Duke Institute of Brain Sciences, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
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10
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Oatman SR, Reddy JS, Quicksall Z, Carrasquillo MM, Wang X, Liu CC, Yamazaki Y, Nguyen TT, Malphrus K, Heckman M, Biswas K, Nho K, Baker M, Martens YA, Zhao N, Kim JP, Risacher SL, Rademakers R, Saykin AJ, DeTure M, Murray ME, Kanekiyo T, Dickson DW, Bu G, Allen M, Ertekin-Taner N. Genome-wide association study of brain biochemical phenotypes reveals distinct genetic architecture of Alzheimer's disease related proteins. Mol Neurodegener 2023; 18:2. [PMID: 36609403 PMCID: PMC9825010 DOI: 10.1186/s13024-022-00592-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 12/19/2022] [Indexed: 01/09/2023] Open
Abstract
BACKGROUND Alzheimer's disease (AD) is neuropathologically characterized by amyloid-beta (Aβ) plaques and neurofibrillary tangles. The main protein components of these hallmarks include Aβ40, Aβ42, tau, phosphor-tau, and APOE. We hypothesize that genetic variants influence the levels and solubility of these AD-related proteins in the brain; identifying these may provide key insights into disease pathogenesis. METHODS Genome-wide genotypes were collected from 441 AD cases, imputed to the haplotype reference consortium (HRC) panel, and filtered for quality and frequency. Temporal cortex levels of five AD-related proteins from three fractions, buffer-soluble (TBS), detergent-soluble (Triton-X = TX), and insoluble (Formic acid = FA), were available for these same individuals. Variants were tested for association with each quantitative biochemical measure using linear regression, and GSA-SNP2 was used to identify enriched Gene Ontology (GO) terms. Implicated variants and genes were further assessed for association with other relevant variables. RESULTS We identified genome-wide significant associations at seven novel loci and the APOE locus. Genes and variants at these loci also associate with multiple AD-related measures, regulate gene expression, have cell-type specific enrichment, and roles in brain health and other neuropsychiatric diseases. Pathway analysis identified significant enrichment of shared and distinct biological pathways. CONCLUSIONS Although all biochemical measures tested reflect proteins core to AD pathology, our results strongly suggest that each have unique genetic architecture and biological pathways that influence their specific biochemical states in the brain. Our novel approach of deep brain biochemical endophenotype GWAS has implications for pathophysiology of proteostasis in AD that can guide therapeutic discovery efforts focused on these proteins.
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Affiliation(s)
- Stephanie R. Oatman
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Joseph S. Reddy
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Zachary Quicksall
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | | | - Xue Wang
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Chia-Chen Liu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yu Yamazaki
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Thuy T. Nguyen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kimberly Malphrus
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Michael Heckman
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
| | - Kristi Biswas
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Kwangsik Nho
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
| | - Matthew Baker
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Yuka A. Martens
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Na Zhao
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Jun Pyo Kim
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Shannon L. Risacher
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
| | - Rosa Rademakers
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
| | - Andrew J. Saykin
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
| | - Michael DeTure
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Melissa E. Murray
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Takahisa Kanekiyo
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - for the Alzheimer’s Disease Neuroimaging Initiative
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Quantitative Health Sciences, Mayo Clinic, Jacksonville, FL USA
- Indiana Alzheimer Disease Center, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN USA
- School of Informatics and Computing, Indiana University School of Medicine, Indianapolis, IN USA
- VIB-UA Center for Molecular Neurology, VIB, University of Antwerp, Antwerp, Belgium
- Department of Medical and Molecular Genetics, Indiana University School of Medicine, Indianapolis, IN USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
| | - Dennis W. Dickson
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Guojun Bu
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Mariet Allen
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic, 4500 San Pablo Road, Jacksonville, FL 32224 USA
- Department of Neurology, Mayo Clinic, 4500 San Pablo Road, Birdsall 3, Jacksonville, FL 32224 USA
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11
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Huguenard CJC, Cseresznye A, Darcey T, Nkiliza A, Evans JE, Hazen SL, Mullan M, Crawford F, Abdullah L. Age and APOE affect L-carnitine system metabolites in the brain in the APOE-TR model. Front Aging Neurosci 2023; 14:1059017. [PMID: 36688151 PMCID: PMC9853982 DOI: 10.3389/fnagi.2022.1059017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/12/2022] [Indexed: 01/07/2023] Open
Abstract
With age the apolipoprotein E (APOE) E4 allele (involved in lipid homeostasis) is associated with perturbation of bioenergetics pathways in Alzheimer's disease (AD). We therefore hypothesized that in aging mice APOE genotype would affect the L-carnitine system (central to lipid bioenergetics), in the brain and in the periphery. Using liquid chromatography-mass spectrometry, levels of L-carnitine and associated metabolites: γ-butyrobetaine (GBB), crotonobetaine, as well as acylcarnitines, were evaluated at 10-, 25-, and 50-weeks, in the brain and the periphery, in a targeted replacement mouse model of human APOE (APOE-TR). Aged APOE-TR mice were also orally administered 125 mg/kg of L-carnitine daily for 7 days followed by evaluation of brain, liver, and plasma L-carnitine system metabolites. Compared to E4-TR, an age-dependent increase among E2- and E3-TR mice was detected for medium- and long-chain acylcarnitines (MCA and LCA, respectively) within the cerebrovasculature and brain parenchyma. While following L-carnitine oral challenge, E4-TR mice had higher increases in the L-carnitine metabolites, GBB and crotonobetaine in the brain and a reduction of plasma to brain total acylcarnitine ratios compared to other genotypes. These studies suggest that with aging, the presence of the E4 allele may contribute to alterations in the L-carnitine bioenergetic system and to the generation of L-carnitine metabolites that could have detrimental effects on the vascular system. Collectively the E4 allele and aging may therefore contribute to AD pathogenesis through aging-related lipid bioenergetics as well as cerebrovascular dysfunctions.
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Affiliation(s)
- Claire J. C. Huguenard
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
- School of Life, Health and Chemical Sciences, Open University, Milton Keynes, United Kingdom
| | - Adam Cseresznye
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
| | - Teresa Darcey
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
| | - Aurore Nkiliza
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
- James A. Haley VA Hospital, Tampa, FL, United States
| | - James E. Evans
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
| | - Stanley L. Hazen
- Department of Cardiovascular and Metabolic Sciences, Cleveland Clinic, Cleveland, OH, United States
| | - Michael Mullan
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
- School of Life, Health and Chemical Sciences, Open University, Milton Keynes, United Kingdom
| | - Fiona Crawford
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
- School of Life, Health and Chemical Sciences, Open University, Milton Keynes, United Kingdom
- James A. Haley VA Hospital, Tampa, FL, United States
| | - Laila Abdullah
- Department of Metabolomics, Roskamp Institute, Sarasota, FL, United States
- School of Life, Health and Chemical Sciences, Open University, Milton Keynes, United Kingdom
- James A. Haley VA Hospital, Tampa, FL, United States
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12
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Fan L, Zhu X, Borenstein AR, Huang X, Shrubsole MJ, Dugan LL, Dai Q. Association of Circulating Caprylic Acid with Risk of Mild Cognitive Impairment and Alzheimer's Disease in the Alzheimer's Disease Neuroimaging Initiative (ADNI) Cohort. J Prev Alzheimers Dis 2023; 10:513-522. [PMID: 37357292 PMCID: PMC10442865 DOI: 10.14283/jpad.2023.37] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/27/2023]
Abstract
OBJECTIVE Medium-chain fatty acids (MCFAs) can rapidly cross the blood-brain barrier and provide an alternative energy source for the brain. This study aims to determine 1) whether plasma caprylic acid (C8:0) is associated with risk of incident mild cognitive impairment (MCI) among baseline cognitively normal (CN) participants, and incident Alzheimer's Disease (AD) among baseline MCI participants; and 2) whether these associations differ by sex, comorbidity of cardiometabolic diseases, apolipoprotein E (APOE) ε4 alleles, and ADAS-Cog 13. METHODS Within the Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort, plasma C8:0 was measured at baseline in 618 AD-free participants aged 55 to 91. Logistic regression models were used to estimate odds ratios (ORs) and 95% CIs with incident MCI and AD as dependent variables, separately. RESULTS The inverse association between circulating C8:0 and risk of incident MCI was of borderline significance. The inverse association between circulating levels of C8:0 and risk of incident MCI was significant among CN participants with ≥1 cardiometabolic diseases [OR (95% CI): 0.75 (0.58-0.98) (P=0.03)], those with one copy of APOE ε4 alleles [OR (95% CI): 0.43 (0.21-0.89) (P=0.02)], female [OR (95% CI): 0.60 (0.38-0.94) (P=0.02)], and ADAS-Cog 13 above the median [OR (95%CI): 0.69 (0.50-0.97)(P=0.03)] after adjusting for all covariates. CONCLUSION The inverse associations were present only among subgroups of CN participants, including female individuals, those with one or more cardiometabolic diseases, or one APOE ε4 allele, or higher ADAS-Cog 13 scores. If confirmed, this finding will facilitate precision prevention of MCI, in turn, AD among CN older adults.
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Affiliation(s)
- L Fan
- Qi Dai, M.D., Ph.D., Department of Medicine, Vanderbilt University Medical Center, 2525 West End Avenue, Suite 800, Nashville, TN 37203-1738, USA, Phone: (615) 936-0707, Fax: (615) 343-5938, E-mail:
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13
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Parker DC, Kraus WE, Whitson HE, Kraus VB, Smith PJ, Cohen HJ, Pieper CF, Faldowski RA, Hall KS, Huebner JL, Ilkayeva OR, Bain JR, Newby LK, Huffman KM. Tryptophan Metabolism and Neurodegeneration: Longitudinal Associations of Kynurenine Pathway Metabolites with Cognitive Performance and Plasma Alzheimer's Disease and Related Dementias Biomarkers in the Duke Physical Performance Across the LifeSpan Study. J Alzheimers Dis 2023; 91:1141-1150. [PMID: 36565121 PMCID: PMC10074831 DOI: 10.3233/jad-220906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND The kynurenine pathway (KP) comprises a family of tryptophan-derived metabolites that some studies have reported are associated with poorer cognitive performance and an increased risk of Alzheimer's disease and related dementias (ADRD). OBJECTIVE The objective of this study was to determine the associations of plasma KP metabolites (kynurenine [KYN], kynurenic acid [KA], and tryptophan [TRP]) with a panel of plasma ADRD biomarkers (Aβ42/ β40 ratio, pTau-181, glial fibrillary acidic protein [GFAP], and neurofilament light [NfL]) and cognitive performance in a subset of older adults drawn from the Duke Physical Performance Across the LifeSpan (PALS) study. METHODS The Montreal Cognitive Assessment (MoCA) was used to assess cognitive performance. We used multivariate multiple regression to evaluate associations of the KYN/TRP and KA/KYN ratios with MoCA score and plasma ADRD biomarkers at baseline and over two years (n = 301; Age = 74.8±8.7). RESULTS Over two years, an increasing KYN/TRP ratio was associated with increasing plasma concentrations of plasma p-Tau181 (β= 6.151; 95% CI [0.29, 12.01]; p = 0.040), GFAP (β= 11.12; 95% CI [1.73, 20.51]; p = 0.020), and NfL (β= 11.13; 95% CI [2.745, 19.52]; p = 0.009), but not MoCA score or the Aβ42/Aβ40 ratio. There were no significant associations of KA/KYN with MoCA score or plasma ADRD biomarkers. CONCLUSION Our findings provide evidence that greater concentrations of KP metabolites are associated longitudinally over two years with greater biomarker evidence of neurofibrillary tau pathology (pTau-181), neuroinflammation (GFAP), and neurodegeneration (NfL), suggesting that dysregulated KP metabolism may play a role in ADRD pathogenesis.
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Affiliation(s)
- Daniel C Parker
- Duke University School of Medicine, Division of Geriatrics, Durham, NC, USA
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
| | - William E Kraus
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Duke University School of Medicine, Division of Cardiology, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
| | - Heather E Whitson
- Duke University School of Medicine, Division of Geriatrics, Durham, NC, USA
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
| | - Virginia B Kraus
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
- Duke University School of Medicine, Division of Rheumatology and Immunology, Durham, NC, USA
| | - Patrick J Smith
- Department of Psychiatry, University of North Carolina, Chapel Hill, Chapel Hill, NC, USA
| | - Harvey Jay Cohen
- Duke University School of Medicine, Division of Geriatrics, Durham, NC, USA
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
| | - Carl F Pieper
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Richard A Faldowski
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, USA
| | - Katherine S Hall
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
- Geriatric Research, Education, and Clinical Center, Veterans Affairs Medical Center, Durham, NC, USA
| | - Janet L Huebner
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
| | - Olga R Ilkayeva
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University School of Medicine, Durham, NC, USA
- Department of Medicine, Duke University School of Medicine, Division of Endocrinology, Metabolism, and Nutrition, Durham, NC, USA
| | - James R Bain
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Claude D. Pepper Older Americans Independence Center, Duke University School of Medicine, Durham, NC, USA
- Sarah W. Stedman Nutrition and Metabolism Center, Duke University School of Medicine, Durham, NC, USA
- Department of Medicine, Duke University School of Medicine, Division of Endocrinology, Metabolism, and Nutrition, Durham, NC, USA
| | - L Kristin Newby
- Duke University School of Medicine, Division of Cardiology, Durham, NC, USA
- Duke University Clinical and Translational Science Institute, Durham, NC, USA
| | - Kim M Huffman
- Duke University Center for the Study of Aging and Human Development, Durham, NC, USA
- Duke Molecular Physiology Institute, Duke University School of Medicine, Durham, NC, USA
- Duke University School of Medicine, Division of Rheumatology and Immunology, Durham, NC, USA
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14
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Zou C, Huang X, Zhang Y, Pan M, Xie J, Chen L, Meng Y, Zou D, Luo J. Potential biomarkers of Alzheimer’s disease and cerebral small vessel disease. Front Mol Neurosci 2022; 15:996107. [PMID: 36299860 PMCID: PMC9588985 DOI: 10.3389/fnmol.2022.996107] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 09/26/2022] [Indexed: 12/02/2022] Open
Abstract
Background Cerebral small vessel disease (CSVD) is associated with the pathogenesis of Alzheimer’s disease (AD). Effective treatments to alleviate AD are still not currently available. Hence, we explored markers and underlying molecular mechanisms associated with AD by utilizing gene expression profiles of AD and CSVD patients from public databases, providing more options for early diagnosis and its treatment. Methods Gene expression profiles were collected from GSE63060 (for AD) and GSE162790 (for CSVD). Differential analysis was performed between AD and mild cognitive impairment (MCI) or CSVD progression and CSVD no-progression. In both datasets, differentially expressed genes (DEGs) with the same expression direction were identified as common DEGs. Then protein-protein interaction (PPI) network was constructed for common DEGs. Differential immune cells and checkpoints were calculated between AD and MCI. Results A total of 146 common DEGs were identified. Common DEGs were mainly enriched in endocytosis and oxytocin signaling pathways. Interestingly, endocytosis and metabolic pathways were shown both from MCI to AD and from CSVD no-progression to CSVD progression. Moreover, SIRT1 was identified as a key gene by ranking degree of connectivity in the PPI network. SIRT1 was associated with obesity-related genes and metabolic disorders. Additionally, SIRT1 showed correlations with CD8 T cells, NK CD56 bright cells, and checkpoints in AD. Conclusion The study revealed that the progression of AD is associated with abnormalities in gene expression and metabolism and that the SIRT1 gene may serve as a promising therapeutic target for the treatment of AD.
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Affiliation(s)
- Chun Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Xiaohua Huang
- Department of Neurology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, China
| | - Yilong Zhang
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Mika Pan
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Jieqiong Xie
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Liechun Chen
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Youshi Meng
- Department of Neurology, The Fifth Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Donghua Zou
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
- Clinical Research Center, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
- *Correspondence: Donghua Zou,
| | - Jiefeng Luo
- Department of Neurology, The Second Affiliated Hospital of Guangxi Medical University, Nanning, China
- Jiefeng Luo,
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15
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Kadyrov M, Whiley L, Brown B, Erickson KI, Holmes E. Associations of the Lipidome with Ageing, Cognitive Decline and Exercise Behaviours. Metabolites 2022; 12:metabo12090822. [PMID: 36144226 PMCID: PMC9505967 DOI: 10.3390/metabo12090822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/16/2022] Open
Abstract
One of the most recognisable features of ageing is a decline in brain health and cognitive dysfunction, which is associated with perturbations to regular lipid homeostasis. Although ageing is the largest risk factor for several neurodegenerative diseases such as dementia, a loss in cognitive function is commonly observed in adults over the age of 65. Despite the prevalence of normal age-related cognitive decline, there is a lack of effective methods to improve the health of the ageing brain. In light of this, exercise has shown promise for positively influencing neurocognitive health and associated lipid profiles. This review summarises age-related changes in several lipid classes that are found in the brain, including fatty acyls, glycerolipids, phospholipids, sphingolipids and sterols, and explores the consequences of age-associated pathological cognitive decline on these lipid classes. Evidence of the positive effects of exercise on the affected lipid profiles are also discussed to highlight the potential for exercise to be used therapeutically to mitigate age-related changes to lipid metabolism and prevent cognitive decline in later life.
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Affiliation(s)
- Maria Kadyrov
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Correspondence: (M.K.); (B.B.); (E.H.)
| | - Luke Whiley
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Perron Institute for Neurological and Translational Science, Nedlands, WA 6009, Australia
| | - Belinda Brown
- Discipline of Exercise Science, College of Science, Health, Engineering and Education, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- Centre for Healthy Ageing, Health Futures Institute, Murdoch University, 90 South Street, Murdoch, WA 6150, Australia
- School of Medical Sciences, Sarich Neuroscience Research Institute, Edith Cowan University, Nedlands, WA 6009, Australia
- Correspondence: (M.K.); (B.B.); (E.H.)
| | - Kirk I. Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15260, USA
- AdventHealth Research Institute, Neuroscience Institute, Orlando, FL 32804, USA
- PROFITH “PROmoting FITness and Health Through Physical Activity” Research Group, Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports, Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain
| | - Elaine Holmes
- Australian National Phenome Centre, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Centre for Computational and Systems Medicine, Health Futures Institute, Murdoch University, Harry Perkins Building, 5 Robin Warren Drive, Murdoch, WA 6150, Australia
- Division of Integrative Systems and Digestive Medicine, Department of Surgery and Cancer, Imperial College London, London SW7 2AZ, UK
- Correspondence: (M.K.); (B.B.); (E.H.)
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16
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Plasma Metabolite Markers of Parkinson's Disease and Atypical Parkinsonism. Metabolites 2021; 11:metabo11120860. [PMID: 34940618 PMCID: PMC8706715 DOI: 10.3390/metabo11120860] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Revised: 11/24/2021] [Accepted: 12/06/2021] [Indexed: 01/26/2023] Open
Abstract
Differentiating between Parkinson’s disease (PD) and the atypical Parkinsonian disorders of multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) is difficult clinically due to overlapping symptomatology, especially at early disease stages. Consequently, there is a need to identify metabolic markers for these diseases and to develop them into viable biomarkers. In the present investigation, solution nuclear magnetic resonance and mass spectrometry metabolomics were used to quantitatively characterize the plasma metabolomes (a total of 167 metabolites) of a cohort of 94 individuals comprising 34 PD, 12 MSA, and 17 PSP patients, as well as 31 control subjects. The distinct and statistically significant differences observed in the metabolite concentrations of the different disease and control groups enabled the identification of potential plasma metabolite markers of each disorder and enabled the differentiation between the disorders. These group-specific differences further implicate disturbances in specific metabolic pathways. The two metabolites, formic acid and succinate, were altered similarly in all three disease groups when compared to the control group, where a reduced level of formic acid suggested an effect on pyruvate metabolism, methane metabolism, and/or the kynurenine pathway, and an increased succinate level suggested an effect on the citric acid cycle and mitochondrial dysfunction.
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