Literature DB >> 33814434

Lipidomic Network of Mild Cognitive Impairment from the Mayo Clinic Study of Aging.

Xuewei Wang1, Hai Bui2, Prashanthi Vemuri3, Jonathan Graff-Radford4, Clifford R Jack3, Ronald C Petersen1,4, Michelle M Mielke1,4.   

Abstract

BACKGROUND: Lipid alterations contribute to Alzheimer's disease (AD) pathogenesis. Lipidomics studies could help systematically characterize such alterations and identify potential biomarkers.
OBJECTIVE: To identify lipids associated with mild cognitive impairment and amyloid-β deposition, and to examine lipid correlation patterns within phenotype groups
Methods: Eighty plasma lipids were measured using mass spectrometry for 1,255 non-demented participants enrolled in the Mayo Clinic Study of Aging. Individual lipids associated with mild cognitive impairment (MCI) were first identified. Correlation network analysis was then performed to identify lipid species with stable correlations across conditions. Finally, differential correlation network analysis was used to determine lipids with altered correlations between phenotype groups, specifically cognitively unimpaired versus MCI, and with elevated brain amyloid versus without.
RESULTS: Seven lipids were associated with MCI after adjustment for age, sex, and APOE4. Lipid correlation network analysis revealed that lipids from a few species correlated well with each other, demonstrated by subnetworks of these lipids. 177 lipid pairs differently correlated between cognitively unimpaired and MCI patients, whereas 337 pairs of lipids exhibited altered correlation between patients with and without elevated brain amyloid. In particular, 51 lipid pairs showed correlation alterations by both cognitive status and brain amyloid. Interestingly, the lipids central to the network of these 51 lipid pairs were not significantly associated with either MCI or amyloid, suggesting network-based approaches could provide biological insights complementary to traditional association analyses.
CONCLUSION: Our attempt to characterize the alterations of lipids at network-level provides additional insights beyond individual lipids, as shown by differential correlations in our study.

Entities:  

Keywords:  Aging; Alzheimer’s disease; amyloid; lipid; lipidomics; mild cognitive impairment; network analysis; systems biology

Mesh:

Substances:

Year:  2021        PMID: 33814434      PMCID: PMC8154710          DOI: 10.3233/JAD-201347

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.160


  33 in total

Review 1.  Dysregulation of lipids in Alzheimer's disease and their role as potential biomarkers.

Authors:  Matthew W Wong; Nady Braidy; Anne Poljak; Russell Pickford; Madhav Thambisetty; Perminder S Sachdev
Journal:  Alzheimers Dement       Date:  2017-02-24       Impact factor: 21.566

Review 2.  The application of lipidomics to biomarker research and pathomechanisms in Alzheimer's disease.

Authors:  Matthew W Wong; Nady Braidy; Anne Poljak; Perminder S Sachdev
Journal:  Curr Opin Psychiatry       Date:  2017-03       Impact factor: 4.741

3.  Analysis of sphingolipids in extracted human plasma using liquid chromatography electrospray ionization tandem mass spectrometry.

Authors:  Hai H Bui; Jennifer K Leohr; Ming-Shang Kuo
Journal:  Anal Biochem       Date:  2012-01-31       Impact factor: 3.365

Review 4.  Mild cognitive impairment as a diagnostic entity.

Authors:  R C Petersen
Journal:  J Intern Med       Date:  2004-09       Impact factor: 8.989

5.  Comparison of 18F-FDG and PiB PET in cognitive impairment.

Authors:  Val J Lowe; Bradley J Kemp; Clifford R Jack; Matthew Senjem; Stephen Weigand; Maria Shiung; Glenn Smith; David Knopman; Bradley Boeve; Brian Mullan; Ronald C Petersen
Journal:  J Nucl Med       Date:  2009-05-14       Impact factor: 10.057

6.  The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics.

Authors:  Rosebud O Roberts; Yonas E Geda; David S Knopman; Ruth H Cha; V Shane Pankratz; Bradley F Boeve; Robert J Ivnik; Eric G Tangalos; Ronald C Petersen; Walter A Rocca
Journal:  Neuroepidemiology       Date:  2008-02-07       Impact factor: 3.282

Review 7.  Differential network biology.

Authors:  Trey Ideker; Nevan J Krogan
Journal:  Mol Syst Biol       Date:  2012-01-17       Impact factor: 11.429

8.  DGCA: A comprehensive R package for Differential Gene Correlation Analysis.

Authors:  Andrew T McKenzie; Igor Katsyv; Won-Min Song; Minghui Wang; Bin Zhang
Journal:  BMC Syst Biol       Date:  2016-11-15

9.  Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study.

Authors:  Vijay R Varma; Anup M Oommen; Sudhir Varma; Ramon Casanova; Yang An; Ryan M Andrews; Richard O'Brien; Olga Pletnikova; Juan C Troncoso; Jon Toledo; Rebecca Baillie; Matthias Arnold; Gabi Kastenmueller; Kwangsik Nho; P Murali Doraiswamy; Andrew J Saykin; Rima Kaddurah-Daouk; Cristina Legido-Quigley; Madhav Thambisetty
Journal:  PLoS Med       Date:  2018-01-25       Impact factor: 11.069

10.  Association between Plasma Ceramides and Phosphatidylcholines and Hippocampal Brain Volume in Late Onset Alzheimer's Disease.

Authors:  Min Kim; Alejo Nevado-Holgado; Luke Whiley; Stuart G Snowden; Hilkka Soininen; Iwona Kloszewska; Patrizia Mecocci; Magda Tsolaki; Bruno Vellas; Madhav Thambisetty; Richard J B Dobson; John F Powell; Michelle K Lupton; Andy Simmons; Latha Velayudhan; Simon Lovestone; Petroula Proitsi; Cristina Legido-Quigley
Journal:  J Alzheimers Dis       Date:  2017       Impact factor: 4.472

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