Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals

Alzheimer's and Dementia

Volumn 12, Issue 7, 2016, Pages 815-822

  Casanova, Ramon ^a   Varma, Sudhir ^b   Simpson, Brittany ^c,d   Kim, Min ^e   An, Yang ^c   Saldana, Santiago ^a   Riveros, Carlos ^f   Moscato, Pablo ^f   Griswold, Michael ^g   Sonntag, Denise ^h   Wahrheit, Judith ^h   Klavins, Kristaps ^h   Jonsson, Palmi V ⁱ   Eiriksdottir, Gudny ^j   Aspelund, Thor ^i,j   Launer, Lenore J ^c   Gudnason, Vilmundur ^i,j   Legido Quigley, Cristina ^e   Thambisetty, Madhav ^c  

^a WAKE FOREST SCHOOL OF MEDICINE   (United States)

^b HiThru Analytics LLC   (United States)

^c NATIONAL INSTITUTE ON AGING   (United States)

^d UNIVERSITY OF MISSISSIPPI SCHOOL OF MEDICINE   (United States)

^e KING'S COLLEGE LONDON   (United Kingdom)

^f UNIVERSITY OF NEWCASTLE   (Australia)

^g UNIVERSITY OF MISSISSIPPI MEDICAL CENTER   (United States)

^h BIOCRATES LIFE SCIENCES AG   (Austria)

ⁱ UNIVERSITY OF ICELAND   (Iceland)

^j ICELANDIC HEART ASSOCIATION   (Iceland)

more..

Author keywords

Biomarker; Machine learning; Metabolomics; Phospholipids; Preclinical Alzheimer's disease

Indexed keywords

BIOLOGICAL MARKER;

ACCURACY; AGED; AGING; ALZHEIMER DISEASE; ARTICLE; COHORT ANALYSIS; CONTROLLED STUDY; FEMALE; HUMAN; LONGITUDINAL STUDY; MACHINE LEARNING; MAJOR CLINICAL STUDY; MALE; METABOLITE; METABOLOMICS; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SENSITIVITY AND SPECIFICITY; VERY ELDERLY; BLOOD; MARYLAND; PROCEDURES; PRODROMAL SYMPTOM;

AGED; AGING; ALZHEIMER DISEASE; BALTIMORE; BIOMARKERS; HUMANS; LONGITUDINAL STUDIES; METABOLOMICS; PRODROMAL SYMPTOMS;

EID: 84960884135     PISSN: 15525260     EISSN: 15525279     Source Type: Journal    
DOI: 10.1016/j.jalz.2015.12.008     Document Type: Article

References (27)

1. [1] Albert, M.S., DeKosky, S.T., Dickson, D., Dubois, B., Feldman, H.H., Fox, N.C., 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 (2011), 270–279.
2. [2] McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R. Jr., Kawas, C.H., 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 (2011), 263–269.
3. [3] Sperling, R.A., Aisen, P.S., Beckett, L.A., Bennett, D.A., Craft, S., Fagan, A.M., 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. Alzheimers Dement 7 (2011), 280–292.
4. [4] Trushina, E., Mielke, M.M., Recent advances in the application of metabolomics to Alzheimer's disease. Biochim Biophys Acta 1842 (2014), 1232–1239.
5. [5] Lewis, G.D., Gerszten, R.E., Toward metabolomic signatures of cardiovascular disease. Circ Cardiovasc Genet 3 (2010), 119–121.
6. [6] Greenberg, N., Grassano, A., Thambisetty, M., Lovestone, S., Legido-Quigley, C., A proposed metabolic strategy for monitoring disease progression in Alzheimer's disease. Electrophoresis 30 (2009), 1235–1239.
7. [7] Han, X., Rozen, S., Boyle, S.H., Hellegers, C., Cheng, H., Burke, J.R., et al. Metabolomics in early Alzheimer's disease: Identification of altered plasma sphingolipidome using shotgun lipidomics. PLoS One, 6, 2011, e21643.
8. [8] Oresic, M., Hyotylainen, T., Herukka, S.K., Sysi-Aho, M., Mattila, I., Seppanan-Laakso, T., et al. Metabolome in progression to Alzheimer's disease. Transl Psychiatry, 1, 2011, e57.
9. [9] Sato, Y., Nakamura, T., Aoshima, K., Oda, Y., Quantitative and wide-ranging profiling of phospholipids in human plasma by two-dimensional liquid chromatography/mass spectrometry. Anal Chem 82 (2010), 9858–9864.
10. [10] Sato, Y., Suzuki, I., Nakamura, T., Bernier, F., Aoshima, K., Oda, Y., Identification of a new plasma biomarker of Alzheimer's disease using metabolomics technology. J Lipid Res 53 (2012), 567–576.
11. [11] Trushina, E., Dutta, T., Persson, X.M., Mielke, M.M., Petersen, R.C., Identification of altered metabolic pathways in plasma and CSF in mild cognitive impairment and Alzheimer's disease using metabolomics. PLoS One, 8, 2013, e63644.
12. [12] Whiley, L., Sen, A., Heaton, J., Proitsi, P., Garcia-Gomez, D., Leung, R., et al. Evidence of altered phosphatidylcholine metabolism in Alzheimer's disease. Neurobiol Aging 35 (2014), 271–278.
13. [13] Mapstone, M., Cheema, A.K., Fiandaca, M.S., Zhong, X., Mhyre, T.R., MacArthur, L.H., et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med 20 (2014), 415–418.
14. [14] Ferrucci, L., The Baltimore Longitudinal Study of Aging (BLSA): A 50-year-long journey and plans for the future. J Gerontol A Biol Sci Med Sci 63 (2008), 1416–1419.
15. [15] Harris, T.B., Launer, L.J., Eiriksdottir, G., Kjartansson, O., Jonsson, P.V., Sigurdsson, G., et al. Age, Gene/Environment Susceptibility-Reykjavik Study: Multidisciplinary applied phenomics. Am J Epidemiol 165 (2007), 1076–1087.
16. [16] Kawas, C., Gray, S., Brookmeyer, R., Fozard, J., Zonderman, A., Age-specific incidence rates of Alzheimer's disease: The Baltimore Longitudinal Study of Aging. Neurology 54 (2000), 2072–2077.
17. [17] Shock, N.W., Gerontology Research C. Normal human aging: The Baltimore Longitudinal Study of Aging. 1984, U.S. Dept. of Health and Human Services, Public Health Service, National Institutes of Health, National Institute on Aging, Gerontology Research Center; For sale by the Supt. of Docs., U.S. G.P.O, [Baltimore, Md.]; Washington, D.C.
18. [18] Qiu, C., Cotch, M.F., Sigurdsson, S., Jonsson, P.V., Jonsdottir, M.K., Sveinbjrnsdottir, S., et al. Cerebral microbleeds, retinopathy, and dementia: The AGES-Reykjavik Study. Neurology 75 (2010), 2221–2228.
19. [19] Koal, T., Klavins, K., Seppi, D., Kemmler, G., Humpel, C., Sphingomyelin SM(d18:1/18:0) is significantly enhanced in cerebrospinal fluid samples dichotomized by pathological amyloid-beta42, tau, and phospho-tau-181 levels. J Alzheimers Dis 44 (2015), 1193–1201.
20. [20] Diagnosing dementia This is not spinal tap. ECONOMIST -LONDON- ECONOMIST NEWSPAPER LIMITED. 2014:79.
21. [21] Bjorkqvist, M., Ohlsson, M., Minthon, L., Hansson, O., Evaluation of a previously suggested plasma biomarker panel to identify Alzheimer's disease. PLoS One, 7, 2012, e29868.
22. [22] Ray, S., Britschgi, M., Herbert, C., Takeda-Uchimura, Y., Boxer, A., Blennow, K., et al. Classification and prediction of clinical Alzheimer's diagnosis based on plasma signaling proteins. Nat Med 13 (2007), 1359–1362.
23. [23] Perlis, R.H., Translating biomarkers to clinical practice. Mol Psychiatry 16 (2011), 1076–1087.
24. [24] Yu, Z., Kastenmuller, G., He, Y., Belcredi, P., Moller, G., Prehn, C., et al. Differences between human plasma and serum metabolite profiles. PLoS One, 6, 2011, e21230.
25. [25] Breier, M., Wahl, S., Prehn, C., Fugmann, M., Ferrari, U., Weise, M., et al. Targeted metabolomics identifies reliable and stable metabolites in human serum and plasma samples. PLoS One, 9, 2014, e89728.
26. [26] Thambisetty, M., Lovestone, S., Blood-based biomarkers of Alzheimer's disease: Challenging but feasible. Biomark Med 4 (2010), 65–79.
27. [27] Tzoulaki, I., Ebbels, T.M., Valdes, A., Elliott, P., Ioannidis, J.P., Design and analysis of metabolomics studies in epidemiologic research: A primer on -omic technologies. Am J Epidemiol 180 (2014), 129–139.