Evidence map›Paper›PMID 39878109›Full record

ArticleCurrent Alzheimer research2024

Associations Between Metabolomics Findings and Brain Hypometabolism in Mild Cognitive Impairment and Alzheimer's Disease.

Moein Mir, Parinaz Khosravani, Elham Ramezannezhad, Fatemeh Pourali Saadabad, Marjan Falahati, Mahsa Ghanbarian, Parsa Saberian, Mohammad Sadeghi, Nafise Niknam, Sanaz Eskandari Ghejelou and 3 more

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Article in Current Alzheimer research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

13 authors.

Moein MirGolestan Rheumatology Research Center, Golestan University of Medical Sciences, Gorgan, Iran.ORCID 0000-0002-3609-4905
Parinaz KhosravaniStudent's Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.ORCID 0009-0007-5382-2082
Elham RamezannezhadSchool of Medicine, Isfahan University of Medical Science, Isfahan, Iran.
Fatemeh Pourali SaadabadDepartment of Psychology, Faculty of Humanities and Social Sciences, Khayyam University of Mashhad, Mashhad, Iran.ORCID 0009-0009-6668-9238
Marjan FalahatiDepartment of Pharmaceutical Science, Faculty of Pharmacy, Kermanshah University of Medical Sciences, Kermanshah, Iran.ORCID 0009-0002-7723-1635
Mahsa GhanbarianDepartment of Health Psychology, Faculty of Medical Sciences, Gorgan Branch, Islamic Azad University, Gorgan, Iran.ORCID 0000-0002-9354-4925
Parsa SaberianStudent Research Committee, Faculty of Medicine, Hormozgan University of Medical Sciences, Bandar Abbas, Iran.ORCID 0000-0002-8889-8630
Mohammad SadeghiSchool of Medicine, Tehran University of Medical Sciences, Tehran, Iran.ORCID 0000-0003-0351-7876
Nafise NiknamSchool of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.ORCID 0009-0007-2071-7673
Sanaz Eskandari GhejelouDepartment of Clinical Psychology, Faculty of Psychology and Educational Sciences, Tabriz branch, Islamic Azad University, Tabriz, Iran.ORCID 0009-0008-9995-9866
Masoumeh JafariStudent's Scientific Research Center, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
David GulisashviliDepartment of Diagnostic Radiology & Nuclear Medicine, University of Maryland, School of Medicine, Baltimore, Maryland, USA.
Mahsa MayeliDepartment of Diagnostic Radiology & Nuclear Medicine, University of Maryland, School of Medicine, Baltimore, Maryland, USA.ORCID 0000-0003-4656-156X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlzheimer's disease (AD) is a progressive neurodegenerative disease with rising prevalence due to the aging global population. Existing methods for diagnosing AD are struggling to detect the condition in its earliest and most treatable stages. One early indicator of AD is a substantial decrease in the brain's glucose metabolism. Metabolomics can detect disturbances in biofluids, which may be advantageous for early detection of some AD-related changes. The study aims to predict brain hypometabolism in Alzheimer's disease using metabolomics findings and develop a predictive model based on metabolomic data.

methodsThe data used in this study were acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI) project. We conducted a longitudinal study with three assessment time points to investigate the predictive power of baseline metabolomics for modeling longitudinal fluorodeoxyglucose- positron emission tomography (FDG-PET) trajectory changes in AD patients. A total of 44 participants with AD were included. The Alzheimer's Disease Assessment Scale (ADAS), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB) were used for cognitive assessments. A single global brain hypo-metabolism index was used as the outcome variable.

resultsAcross models, we observed consistent positive relationships between specific cholesterol esters - CE (20:3) (p = 0.005) and CE (18:3) (p = 0.0039) - and FDG-PET metrics, indicating these baseline metabolites may be valuable indicators of future PET score changes. Selected triglycerides like DG-O (16:0-20:4) also showed time-specific positive associations (p = 0.017).

conclusionThis research provides new insights into the disruptions in the metabolic network linked to AD pathology. These findings could pave the way for identifying novel biomarkers and potential treatment targets for AD.

Indexed as

Alzheimer DiseaseBrainCognitive DysfunctionMetabolomicsAgedAged, 80 and overFemaleFluorodeoxyglucose F18HumansLongitudinal StudiesMalePositron-Emission TomographyFluorodeoxyglucose F18ADNI.Alzheimer's Diseasebiomarkersdementiafluorodeoxyglucose-positron emission tomography (FDG PET)metabolomics

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PMID39878109

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.