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Abstract

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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Change history

  • 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.

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Acknowledgements

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.

Author information

Author notes

    • Susan G Fisher
    •  & Dan J Berlau

    Present addresses: Department of Clinical Sciences, Temple University School of Medicine, Philadelphia, Pennsylvania, USA (S.G.F.); Department of Pharmaceutical Sciences, Regis University School of Pharmacy, Denver, Colorado, USA (D.J.B.).

Affiliations

  1. Department of Neurology, University of Rochester School of Medicine, Rochester, New York, USA.

    • Mark Mapstone
  2. Department of Oncology, Georgetown University Medical Center, Washington, DC, USA.

    • Amrita K Cheema
  3. Department of Biochemistry, Georgetown University Medical Center, Washington, DC, USA.

    • Amrita K Cheema
  4. Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.

    • Massimo S Fiandaca
    •  & Howard J Federoff
  5. Department of Neuroscience, Georgetown University Medical Center, Washington, DC, USA.

    • Massimo S Fiandaca
    • , Timothy R Mhyre
    • , Linda H MacArthur
    •  & Howard J Federoff
  6. Department of Biostatistics, Bioinformatics, and Biomathematics, Georgetown University Medical Center, Washington, DC, USA.

    • Xiaogang Zhong
    •  & Ming T Tan
  7. Department of Medicine, University of Rochester School of Medicine, Rochester, New York, USA.

    • William J Hall
  8. Department of Public Health Sciences, University of Rochester School of Medicine, Rochester, New York, USA.

    • Susan G Fisher
  9. Department of Biostatistics and Computational Biology, University of Rochester School of Medicine, Rochester, New York, USA.

    • Derick R Peterson
  10. Department of Medicine, Unity Health System, Rochester, New York, USA.

    • James M Haley
  11. Department of Family Medicine, Unity Health System, Rochester, New York, USA.

    • Michael D Nazar
  12. Division of Long Term Care and Senior Services, Rochester General Hospital, Rochester, New York, USA.

    • Steven A Rich
  13. Department of Neurobiology and Behavior, University of California, Irvine School of Medicine, Irvine, California, USA.

    • Dan J Berlau
    • , Carrie B Peltz
    •  & Claudia H Kawas

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Contributions

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.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Howard J Federoff.

Supplementary information

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    Supplementary Text and Figures

    Supplementary Tables 1–3, Supplementary Figures 1–4 and Supplementary Note.

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DOI

https://doi.org/10.1038/nm.3466