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Plasma phospholipids identify antecedent memory impairment in older adults

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Alzheimer's disease causes a progressive dementia that currently affects over 35 million individuals worldwide and is expected to affect 115 million by 2050 (ref. 1). There are no cures or disease-modifying therapies, and this may be due to our inability to detect the disease before it has progressed to produce evident memory loss and functional decline. Biomarkers of preclinical disease will be critical to the development of disease-modifying or even preventative therapies2. Unfortunately, current biomarkers for early disease, including cerebrospinal fluid tau and amyloid-β levels3, structural and functional magnetic resonance imaging4 and the recent use of brain amyloid imaging5 or inflammaging6, are limited because they are either invasive, time-consuming or expensive. Blood-based biomarkers may be a more attractive option, but none can currently detect preclinical Alzheimer's disease with the required sensitivity and specificity7. Herein, we describe our lipidomic approach to detecting preclinical Alzheimer's disease in a group of cognitively normal older adults. We discovered and validated a set of ten lipids from peripheral blood that predicted phenoconversion to either amnestic mild cognitive impairment or Alzheimer's disease within a 2–3 year timeframe with over 90% accuracy. This biomarker panel, reflecting cell membrane integrity, may be sensitive to early neurodegeneration of preclinical Alzheimer's disease.

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Figure 1: Memory composite z-scores and trend plots for the ten-metabolite panel in the discovery phase.
Figure 2: ROC results for the lipidomics analyses.

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  • 20 June 2014

     In the version of this article initially published online, the Source Data file for Figure 1 contained a transposition error that occurred when the authors were moving data from their analysis software into Excel. This error does not affect the accuracy of the data shown in Figure 1. This error has been corrected in the HTML version of the article.


  1. World Health Organization. Dementia: a Public Health Priority (World Health Organization, Geneva, 2012).

  2. Sperling, R.A. et al. Toward defining the preclinical stages of Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimer's & Dementia: the Journal of the Alzheimer's Association 7, 280–292 (2011).

    Article  Google Scholar 

  3. Hulstaert, F. et al. Improved discrimination of AD patients using β-amyloid(1–42) and tau levels in CSF. Neurology 52, 1555–1562 (1999).

    Article  CAS  Google Scholar 

  4. Small, S.A., Perera, G.M., De La Paz, R., Mayeux, R. & Stern, Y. Differential regional dysfunction of the hippocampal formation among elderly with memory decline and Alzheimer's disease. Ann. Neurol. 45, 466–472 (1999).

    Article  CAS  Google Scholar 

  5. Klunk, W.E. et al. Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B. Ann. Neurol. 55, 306–319 (2004).

    Article  CAS  Google Scholar 

  6. Franceschi, C. et al. Inflamm-aging. An evolutionary perspective on immunosenescence. Ann. NY Acad. Sci. 908, 244–254 (2000).

    Article  CAS  Google Scholar 

  7. Thambisetty, M. & Lovestone, S. Blood-based biomarkers of Alzheimer's disease: challenging but feasible. Biomark. Med. 4, 65–79 (2010).

    Article  CAS  Google Scholar 

  8. Tibshirani, R. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B Stat. Methodol. 58, 267–288 (1996).

    Google Scholar 

  9. Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning; Data Mining, Inference, and Prediction. (Springer-Verlag, New York, 2008).

  10. van Meer, G. & de Kroon, A.I. Lipid map of the mammalian cell. J. Cell Sci. 124, 5–8 (2011).

    Article  CAS  Google Scholar 

  11. Jones, L.L., McDonald, D.A. & Borum, P.R. Acylcarnitines: role in brain. Prog. Lipid Res. 49, 61–75 (2010).

    Article  CAS  Google Scholar 

  12. Nitsch, R.M. et al. Evidence for a membrane defect in Alzheimer disease brain. Proc. Natl. Acad. Sci. USA 89, 1671–1675 (1992).

    Article  CAS  Google Scholar 

  13. Schaefer, E.J. et al. Plasma phosphatidylcholine docosahexaenoic acid content and risk of dementia and Alzheimer disease: the Framingham Heart Study. Arch. Neurol. 63, 1545–1550 (2006).

    Article  Google Scholar 

  14. Mulder, C. et al. Decreased lysophosphatidylcholine/phosphatidylcholine ratio in cerebrospinal fluid in Alzheimer's disease. J. Neural Transm. 110, 949–955 (2003).

    Article  CAS  Google Scholar 

  15. Walter, A. et al. Glycerophosphocholine is elevated in cerebrospinal fluid of Alzheimer patients. Neurobiol. Aging 25, 1299–1303 (2004).

    Article  CAS  Google Scholar 

  16. Prasad, M.R., Lovell, M.A., Yatin, M., Dhillon, H. & Markesbery, W.R. Regional membrane phospholipid alterations in Alzheimer's disease. Neurochem. Res. 23, 81–88 (1998).

    Article  CAS  Google Scholar 

  17. Pettegrew, J.W., Panchalingam, K., Hamilton, R.L. & McClure, R.J. Brain membrane phospholipid alterations in Alzheimer's disease. Neurochem. Res. 26, 771–782 (2001).

    Article  CAS  Google Scholar 

  18. Haughey, N.J., Bandaru, V.V., Bae, M. & Mattson, M.P. Roles for dysfunctional sphingolipid metabolism in Alzheimer's disease neuropathogenesis. Biochim. Biophys. Acta 1801, 878–886 (2010).

    Article  CAS  Google Scholar 

  19. Kordower, J.H. & Fiandaca, M.S. Response of the monkey cholinergic septohippocampal system to fornix transection: a histochemical and cytochemical analysis. J. Comp. Neurol. 298, 443–457 (1990).

    Article  CAS  Google Scholar 

  20. Kordower, J.H. et al. The aged monkey basal forebrain: rescue and sprouting of axotomized basal forebrain neurons after grafts of encapsulated cells secreting human nerve growth factor. Proc. Natl. Acad. Sci. USA 91, 10898–10902 (1994).

    Article  CAS  Google Scholar 

  21. Whitehouse, P.J., Price, D.L., Clark, A.W., Coyle, J.T. & DeLong, M.R. Alzheimer disease: evidence for selective loss of cholinergic neurons in the nucleus basalis. Ann. Neurol. 10, 122–126 (1981).

    Article  CAS  Google Scholar 

  22. Hansson, O. et al. Association between CSF biomarkers and incipient Alzheimer's disease in patients with mild cognitive impairment: a follow-up study. Lancet Neurol. 5, 228–234 (2006).

    Article  CAS  Google Scholar 

  23. Blennow, K., Hampel, H., Weiner, M. & Zetterberg, H. Cerebrospinal fluid and plasma biomarkers in Alzheimer disease. Nat. Rev. Neurol. 6, 131–144 (2010).

    Article  CAS  Google Scholar 

  24. Irizarry, M.C. Biomarkers of Alzheimer disease in plasma. NeuroRx 1, 226–234 (2004).

    Article  Google Scholar 

  25. Ray, S. et al. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat. Med. 13, 1359–1362 (2007).

    Article  CAS  Google Scholar 

  26. Doecke, J.D. et al. Blood-based protein biomarkers for diagnosis of Alzheimer disease. Arch. Neurol. 69, 1318–1325 (2012).

    Article  Google Scholar 

  27. Roe, C.M. et al. Improving CSF biomarker accuracy in predicting prevalent and incident Alzheimer disease. Neurology 76, 501–510 (2011).

    Article  CAS  Google Scholar 

  28. Fagan, A.M. et al. Cerebrospinal fluid tau/β-amyloid42 ratio as a prediction of cognitive decline in nondemented older adults. Arch. Neurol. 64, 343–349 (2007).

    Article  Google Scholar 

  29. Albert, M.S. et al. The diagnosis of mild cognitive impairment due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 270–279 (2011).

    Article  Google Scholar 

  30. Petersen, R.C. et al. Mild cognitive impairment: clinical characterization and outcome. Arch. Neurol. 56, 303–308 (1999).

    Article  CAS  Google Scholar 

  31. Espinosa, A. et al. A longitudinal follow-up of 550 mild cognitive impairment patients: evidence for large conversion to dementia rates and detection of major risk factors involved. J. Alzheimers Dis. 34, 769–780 (2013).

    Article  Google Scholar 

  32. McKhann, G.M. et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease. Alzheimers Dement. 7, 263–269 (2011).

    Article  Google Scholar 

  33. Want, E.J. et al. Global metabolic profiling procedures for urine using UPLC-MS. Nat. Protoc. 5, 1005–1018 (2010).

    Article  CAS  Google Scholar 

  34. Grebe, S.K. & Singh, R.J. LC-MS/MS in the clinical laboratory - where to from here? Clin. Biochem. Rev. 32, 5–31 (2011).

    PubMed  PubMed Central  Google Scholar 

  35. Illig, T. et al. A genome-wide perspective of genetic variation in human metabolism. Nat. Genet. 42, 137–141 (2010).

    Article  CAS  Google Scholar 

  36. Römisch-Margl, W.P.C., Bogumil, R., Röhring, C. & Suhre, K. Procedure for tissue sample preparation and metabolite extraction for high-throughput targeted metabolomics. Metabolomics 7, 1–14 (2011).

    Article  Google Scholar 

  37. Bolstad, B.M., Irizarry, R.A., Astrand, M. & Speed, T.P. A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics 19, 185–193 (2003).

    Article  CAS  Google Scholar 

  38. Ma, S. & Huang, J. Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 21, 4356–4362 (2005).

    Article  CAS  Google Scholar 

  39. Liu, Z. & Tan, M. ROC-based utility function maximization for feature selection and classification with applications to high-dimensional protease data. Biometrics 64, 1155–1161 (2008).

    Article  Google Scholar 

  40. Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).

    Article  Google Scholar 

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We thank E. Johnson and D. Greenia for study coordination; P. Bailie, M. Patel and A. Balasubramanian for assistance with data collection; and A. Almudevar for statistical support. We also thank R. Padilla and I. Conteh for processing the blood samples and R. Singh and P. Kaur for technical assistance in developing the lipidomics data. This work was funded by US National Institutes of Health grants R01AG030753 and DOD W81XWH-09-1-0107 to H.J.F.

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Authors and Affiliations



H.J.F., T.R.M., C.H.K., W.J.H., S.G.F., M.M., M.S.F. and A.K.C. conceived of the study. W.J.H., J.M.H., M.D.N., S.A.R. and C.H.K. recruited participants and provided material support for data collection. M.M., D.J.B. and C.B.P. collected the clinical data. D.R.P., S.G.F. and M.M. derived the cognitive z-score methodology. M.M. completed statistical analysis of the cognitive data. A.K.C., M.S.F., T.R.M. and L.H.M. completed the lipidomics analyses. M.T.T., X.Z. and A.K.C. completed statistical analysis of the lipidomics data. M.M., A.K.C., M.S.F. and H.J.F. wrote the manuscript. All authors edited the manuscript for content.

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Correspondence to Howard J Federoff.

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The authors declare no competing financial interests.

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Mapstone, M., Cheema, A., Fiandaca, M. et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20, 415–418 (2014).

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