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Cited by 65Metabolomics and Mass Spectrometry StudiesDementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Generation and quality control of lipidomics data for the alzheimer’s disease neuroimaging initiative cohort

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Barupal, Dinesh Kumar, Fan, Sili, Wancewicz, Benjamin, Cajka, Tomas, Sa, Michael, Showalter, Megan R., Baillie, Rebecca, Tenenbaum, Jessica D. +5 more·2018·Scientific Data
DOI 10.1038/sdata.2018.263PMC PMC6244184
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Abstract

Alzheimer’s disease (AD) is a major public health priority with a large socioeconomic burden and complex etiology. The Alzheimer Disease Metabolomics Consortium (ADMC) and the Alzheimer Disease Neuroimaging Initiative (ADNI) aim to gain new biological insights in the disease etiology. We report here an untargeted lipidomics of serum specimens of 806 subjects within the ADNI1 cohort (188 AD, 392 mild cognitive impairment and 226 cognitively normal subjects) along with 83 quality control samples. Lipids were detected and measured using an ultra-high-performance liquid chromatography quadruple/time-of-flight mass spectrometry (UHPLC-QTOF MS) instrument operated in both negative and positive electrospray ionization modes. The dataset includes a total 513 unique lipid species out of which 341 are known lipids. For over 95% of the detected lipids, a relative standard deviation of better than 20% was achieved in the quality control samples, indicating high technical reproducibility. Association modeling of this dataset and available clinical, metabolomics and drug-use data will provide novel insights into the AD etiology. These datasets are available at the ADNI repository at http://adni.loni.usc.edu/

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  • Features with a CV >30% across QC injections were removed.

    “Features with a CV >30% across QC injections were removed79,80.”

    From An iPSC-derived astrocyte model of fragile X syndrome exhibits dysregulated cholesterol homeostasis (2023) · cites this paper as [79]

    The Technical validation passage says lipids with technical errors greater than 25% RSD in QC samples were removed, not those exceeding 30% CV. The supplied copy does not give a page number for this passage. (LLM, conf=0.88)

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    Experimental — accuracy not yet measured

  • Features belonging to the same metabolite were assigned a cluster-ID using RAMClust.

    “To aid in the identification of features that belong to the same metabolite, features were assigned a cluster-ID using RAMClust79,81.”

    From An iPSC-derived astrocyte model of fragile X syndrome exhibits dysregulated cholesterol homeostasis (2023) · cites this paper as [79]

    The Methods > LC-MS data processing passages describe targeted ion extraction and selecting one adduct per lipid using quality-control variance. Searches for RAMClust and cluster-ID found no passage saying that features were assigned cluster IDs using RAMClust. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

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Research works printed but not linked: 42

  1. Sage Bionetworks 2017 http://dx.doi.org/10.7303/syn10495782.1 · DOI
  2. Sage Bionetworks 2017 http://dx.doi.org/10.7303/syn10495771.1 · DOI
  3. Sage Bionetworks 2017 http://dx.doi.org/10.7303/syn10495758.2 · DOI
  4. Sage Bionetworks 2017 http://dx.doi.org/10.7303/syn10208594.1 · DOI
  5. McKhann G. et al. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology 34, 939– 944 ((1984). 6610841
  6. Jack C. R. Jr. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207– 216; DOI: 10.1016/S1474-4422(12)70291-0 (2013). 23332364 PMC3622225 · DOI · Paper page
  7. Gotz J. & Ittner L. M. Animal models of Alzheimer’s disease and frontotemporal dementia. Nat Rev Neurosci 9, 532– 544; DOI: 10.1038/nrn2420 (2008). 18568014 · DOI · Paper page
  8. Toledo J. B. et al. Metabolic network failures in Alzheimer’s disease-A biochemical road map. Alzheimers Dement.; DOI: 10.1016/j.jalz.2017.01.020 (2017). PMC5866045 28341160 · DOI
  9. Spillantini M. G. & Goedert M. Tau pathology and neurodegeneration. Lancet Neurol 12, 609– 622,; DOI: 10.1016/S1474-4422(13)70090-5 (2013). 23684085 · DOI · Paper page
  10. McLean C. A. et al. Soluble pool of Abeta amyloid as a determinant of severity of neurodegeneration in Alzheimer’s disease. Ann Neurol. 46, 860– 866 ((1999). 10589538
  11. Zhu F. et al. Biomedical text mining and its applications in cancer research. J Biomed Inform 46, 200– 211; DOI: 10.1016/j.jbi.2012.10.007 (2013). 23159498 · DOI
  12. Ashe K. H. & Zahs K. R. Probing the biology of Alzheimer’s disease in mice. Neuron. 66, 631– 645,; DOI: 10.1016/j.neuron.2010.04.031 (2010). 20547123 PMC2956420 · DOI · Paper page
  13. Liu C. C., Liu C. C., Kanekiyo T., Xu H. & Bu G. Apolipoprotein E and Alzheimer disease: risk, mechanisms and therapy. Nat Rev Neurol 9, 106– 118; DOI: 10.1038/nrneurol.2012.263 (2013). 23296339 PMC3726719 · DOI · Paper page
  14. Quehenberger O. et al. Lipidomics reveals a remarkable diversity of lipids in human plasma. J Lipid Res 51, 3299– 3305; DOI: 10.1194/jlr.M009449 (2010). 20671299 PMC2952570 · DOI
  15. Wenk M. R. Lipidomics: new tools and applications. Cell 143, 888– 895; DOI: 10.1016/j.cell.2010.11.033 (2010). 21145456 · DOI
  16. Wenk M. R. The emerging field of lipidomics. Nat Rev Drug Discov 4, 594– 610; DOI: 10.1038/nrd1776 (2005). 16052242 · DOI
  17. Kaddurah-Daouk R. & Weinshilboum R. Metabolomic Signatures for Drug Response Phenotypes: Pharmacometabolomics Enables Precision Medicine. Clin Pharmacol Ther 98, 71– 75; DOI: 10.1002/cpt.134 (2015). 25871646 PMC4938244 · DOI
  18. Cajka T. & Fiehn O. Comprehensive analysis of lipids in biological systems by liquid chromatography-mass spectrometry. Trends Analyt Chem 61, 192– 206; DOI: 10.1016/j.trac.2014.04.017 (2014). PMC4187118 25309011 · DOI
  19. Weir J. M. et al. Plasma lipid profiling in a large population-based cohort. J Lipid Res 54, 2898– 2908; DOI: 10.1194/jlr.P035808 (2013). 23868910 PMC3770102 · DOI
  20. Jackson S. N., Wang H. Y. & Woods A. S. Direct profiling of lipid distribution in brain tissue using MALDI-TOFMS. Anal Chem. 77, 4523– 4527; DOI: 10.1021/ac050276v (2005). 16013869 · DOI
  21. Ng K. P. et al. Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease. Neurology 88, 1814– 1821; DOI: 10.1212/WNL.0000000000003916 (2017). 28404803 PMC5419982 · DOI
  22. Ossenkoppele R. et al. Tau, amyloid, and hypometabolism in a patient with posterior cortical atrophy. Ann Neurol. 77, 338– 342; DOI: 10.1002/ana.24321 (2015). 25448043 PMC4382124 · DOI
  23. Butterfield D. A., Di Domenico F. & Barone E. Elevated risk of type 2 diabetes for development of Alzheimer disease: a key role for oxidative stress in brain. Biochim Biophys Acta 1842, 1693– 1706; DOI: 10.1016/j.bbadis.2014.06.010 (2014). 24949886 PMC4125611 · DOI · Paper page
  24. Picconi F. et al. Retinal neurodegeneration in patients with type 1 diabetes mellitus: the role of glycemic variability. Acta Diabetol. 54, 489– 497; DOI: 10.1007/s00592-017-0971-4 (2017). 28238189 PMC5385321 · DOI
  25. Verdile G., Fuller S. J. & Martins R. N. The role of type 2 diabetes in neurodegeneration. Neurobiol Dis. 84, 22– 38; DOI: 10.1016/j.nbd.2015.04.008 (2015). 25926349 · DOI
  26. de la Monte S. M. Brain insulin resistance and deficiency as therapeutic targets in Alzheimer’s disease. Curr Alzheimer Res 9, 35– 66 ((2012). 22329651 PMC3349985
  27. Kullmann S. et al. Brain Insulin Resistance at the Crossroads of Metabolic and Cognitive Disorders in Humans. Physiol Rev. 96, 1169– 1209; DOI: 10.1152/physrev.00032.2015 (2016). 27489306 · DOI
  28. Willette A. A., Modanlo N. & Kapogiannis D.Alzheimer’s Disease Neuroimaging, I. Insulin resistance predicts medial temporal hypermetabolism in mild cognitive impairment conversion to Alzheimer disease. Diabetes 64, 1933– 1940; DOI: 10.2337/db14-1507 (2015). 25576061 PMC4439566 · DOI
  29. Proitsi P. et al. Association of blood lipids with Alzheimer’s disease: A comprehensive lipidomics analysis. Alzheimers Dement. 13, 140– 151; DOI: 10.1016/j.jalz.2016.08.003 (2017). 27693183 · DOI
  30. Han X. et al. Metabolomics in early Alzheimer’s disease: identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One 6, e21643; DOI: 10.1371/journal.pone.0021643 (2011). 21779331 PMC3136924 · DOI · Paper page
  31. Proitsi P. et al. Plasma lipidomics analysis finds long chain cholesteryl esters to be associated with Alzheimer’s disease. Transl Psychiatry 5, e494; DOI: 10.1038/tp.2014.127 (2015). 25585166 PMC4312824 · DOI
  32. Weiner M. W. et al. Impact of the Alzheimer’s Disease Neuroimaging Initiative, 2004 to 2014. Alzheimers Dement. 11, 865– 884; DOI: 10.1016/j.jalz.2015.04.005 (2015). 26194320 PMC4659407 · DOI
  33. Mueller S. G. et al. The Alzheimer’s disease neuroimaging initiative. Neuroimaging Clin N Am 15, 869– 877; DOI: 10.1016/j.nic.2005.09.008 (2005). 16443497 PMC2376747 · DOI · Paper page
  34. St John-Williams L. et al. Targeted metabolomics and medication classification data from participants in the ADNI1 cohort. Sci. Data 4, 170140; DOI: 10.1038/sdata.2017.140 (2017). 29039849 PMC5644370 · DOI
  35. Tsugawa H. et al. MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis. Nat Methods. 12, 523– 526; DOI: 10.1038/nmeth.3393 (2015). 25938372 PMC4449330 · DOI
  36. McCoin C. S. et al. Unique plasma metabolomic signatures of individuals with inherited disorders of long-chain fatty acid oxidation. J Inherit Metab Dis 39, 399– 408; DOI: 10.1007/s10545-016-9915-3 (2016). 26907176 PMC4851894 · DOI
  37. Fahrmann J. et al. Systemic alterations in the metabolome of diabetic NOD mice delineate increased oxidative stress accompanied by reduced inflammation and hypertriglyceremia. Am J Physiol Endocrinol Metab 308, E978– E989; DOI: 10.1152/ajpendo.00019.2015 (2015). 25852003 PMC4451288 · DOI
  38. Cajka T. & Fiehn O. Increasing lipidomic coverage by selecting optimal mobile-phase modifiers in LC–MS of blood plasma. Metabolomics 12, 34; DOI: 10.1007/s11306-015-0929-x (2016). · DOI
  39. Cajka T. et al. Using a lipidomics approach for nutritional phenotyping in response to a test meal containing gamma-linolenic acid. Metabolomics 12, 127; DOI: 10.1007/s11306-016-1075-9 (2016). · DOI
  40. Kind T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat Methods 10, 755– 758; DOI: 10.1038/nmeth.2551 (2013). 23817071 PMC3731409 · DOI
  41. Cajka T., Smilowitz J. T. & Fiehn O. Validating Quantitative Untargeted Lipidomics Across Nine Liquid Chromatography-High-Resolution Mass Spectrometry Platforms. Anal Chem. 89, 12360– 12368; DOI: 10.1021/acs.analchem.7b03404 (2017). 29064229 · DOI
  42. Lankinen M. et al. A Healthy Nordic Diet Alters the Plasma Lipidomic Profile in Adults with Features of Metabolic Syndrome in a Multicenter Randomized Dietary Intervention. J Nutr.; DOI: 10.3945/jn.115.220459 (2016). 26962194 · DOI

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Authors
Barupal, D. K., Fan, S., Wancewicz, B., Cajka, T., Sa, M., Showalter, M. R., Baillie, R., Tenenbaum, J. D., Louie, G., Initiative, A. D. N., Alzheimer’s Disease Metabolomics Consortium, Kaddurah-Daouk, R., & Fiehn, O.
Affiliations
  • University of California, Davis (US)
  • Rosa (United States) (US)
  • Duke University (US)
  • King Abdulaziz University (SA)
Corresponding author
  • Kaddurah-Daouk, Rima
  • Fiehn, Oliver
ORCID
Venue
Scientific Data
Publisher
Springer Science and Business Media LLC
Published
2018-11-20
Volume
5
Issue
1
Pages
180263-180263
ISSN
2052-4463 (ISSN-L)
DOI
10.1038/sdata.2018.263
PMC
PMC6244184
OpenAlex
W2901044913
Cited by
65
References
9
Citations by year
2026: 4 · 2025: 7 · 2024: 1 · 2023: 14 · 2022: 12 · 2021: 7 · 2020: 11 · 2019: 8 · 2018: 1
Type
data paper
Language
en
Open access
Licence
Locations
  • Scientific Data (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • eScholarship (California Digital Library) (submittedVersion, cc-by)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • U.S. Department of Defense (U01 AG024904, W81XWH, W81XWH-12-2-, W81XWH-12, W81XWH1220012, AG024904)
  • Foundation for the National Institutes of Health (AG017586, P30 AG010124, W81XWH-12-2-0012, U01AG024904, Grant U01 AG024904, AG010124, AG024904, P50 NS053488)
  • Alzheimer's Association (P30AG010124, U01 AG024904, W81XWH-12-2-0012)
  • Bristol-Myers Squibb (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Eli Lilly and Company (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Pfizer (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • Biogen (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • University of Southern California (W81XWH-12-2-0012, U01 AG024904)
  • F. Hoffmann-La Roche (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • Meso Scale Diagnostics (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • Alzheimer's Disease Neuroimaging Initiative (W81XWH-12-2–0012, U01AG024904, AG024904, W81XWH-12-2-, Department of Defense award number W81XWH-12-2-0012)
  • BioClinica (U01 AG024904, W81XWH-12-2-0012, AG024904)
  • Novartis Pharmaceuticals Corporation (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Northern California Institute for Research and Education (U01 AG024904, W81XWH-12-2-0012)
  • Eisai (W81XWH-12-2-0012, U01 AG024904, AG024904)
  • Servier (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • H. Lundbeck A/S (U01 AG024904, AG024904, W81XWH-12-2-0012)
  • IXICO (AG024904, W81XWH-12-2-0012, U01 AG024904)
  • National Institutes of Health (3u01ag024904-10s2, W81XWH, W81XWH-12-2-0012, AG010124, U01-AG024904, Grant U01 AG024904, NS053488, AG024904, AG017586)
  • Genentech (W81XWH-12-2-0012, AG024904, U01 AG024904)
  • University of California, San Diego
  • Canadian Institutes of Health Research (U01 AG024904, W81XWH-12-2-0012)
  • National Institute on Aging (U01-AG-024904, 3U01 AG024904-09S4, W81XWH-12-2–0012, AG-010124, AG 024904, P30AG010124, P01AG017586, AG032953, PO1 AG017586, AG-017586, R01AG046171, P01AG032953, 3U01AG024904)
  • National Institute of Diabetes and Digestive and Kidney Diseases (U24DK097154)
  • National Institute of Neurological Disorders and Stroke (P50NS053488)
  • National Institute of Biomedical Imaging and Bioengineering (W81XWH-12-2-0012, U01 AG024904, AG024904)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, lipidomics, serum lipids, untargeted lipid profiling, mild cognitive impairment, quality control, technical reproducibility, ADNI cohort, metabolomics
MeSH terms
Aged, Aged, 80 and over, Alzheimer Disease, Humans, Lipids, Mass Spectrometry, Cohort Studies, Metabolomics, Neuroimaging, Cognitive Dysfunction

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