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31 - Blood Biomarkers of Cognitive Health and Neurodegenerative Disease

from Part IV - Cognitive, Social, and Biological Factors across the Lifespan

Published online by Cambridge University Press:  28 May 2020

Ayanna K. Thomas
Affiliation:
Tufts University, Massachusetts
Angela Gutchess
Affiliation:
Brandeis University, Massachusetts
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Summary

The aging of any biological system results in quantifiable change which may affect the output of the system in subtle or substantial ways. Human cognitive aging is no exception and the manner in which the system, in this case the brain, is able to withstand and/or adapt to the effects of age-related physiological change will determine the individual cognitive trajectory. In this chapter, we review the emerging field of blood biomarkers of cognitive aging with a focus on specific metabolic pathways implicated in cognitive health including cellular energetics, lipid metabolism, the maintenance of redox state, and inflammation. Challenges to blood biomarker development, including methodological and inferential limitations, are also reviewed. Ultimately, blood biomarkers of age-related neurodegenerative disease and cognitive success will provide clues for how we might all age successfully, reducing health care burden on societies and improving quality of life for individuals.

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The Cambridge Handbook of Cognitive Aging
A Life Course Perspective
, pp. 568 - 586
Publisher: Cambridge University Press
Print publication year: 2020

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References

Albert, M. S., DeKosky, S. T., Dickson, D., et al. (2011). 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. Alzheimer’s and Dementia, 7(3), 270279. https://doi.org/10.1016/j.jalz.2011.03.008Google Scholar
Alexander, S. (1920). Space, time, and deity: The Gifford lectures at Glasgow, 1916–1918, Vol. 2. London: Macmillan.Google Scholar
Amor, S., Puentes, F., Baker, D., & Van Der Valk, P. (2010). Inflammation in neurodegenerative diseases. Immunology, 129(2), 154169. https://doi.org/10.1111/j.1365-2567.2009.03225.xCrossRefGoogle ScholarPubMed
Andersen, J. K. (2004). Oxidative stress in neurodegeneration: Cause or consequence? Nature Medicine, 10(7), S18S25. https://doi.org/10.1038/nrn1434CrossRefGoogle ScholarPubMed
Balasubramanian, A. B., Kawas, C. H., Peltz, C. B., Brookmeyer, R., & Corrada, M. M. (2012). Alzheimer disease pathology and longitudinal cognitive performance in the oldest-old with no dementia. Neurology, 79(9), 915921. https://doi.org/10.1212/WNL.0b013e318266fc77Google Scholar
Bateman, R. J., Xiong, C., Benzinger, T. L., et al. (2012). Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine, 367(9), 795804. https://doi.org/10.1056/NEJMoa1202753Google Scholar
Biomarkers Definitions Working Group (2001). Biomarkers and surrogate endpoints: Preferred definitions and conceptual framework. Clinical Pharmacology and Therapeutics, 69(3), 8995. https://doi.org/10.1067/mcp.2001.113989Google Scholar
Bishop, N. A., Lu, T., & Yankner, B. A. (2010). Neural mechanisms of ageing and cognitive decline. Nature, 464(7288), 529535. https://doi.org/10.1038/nature08983Google Scholar
Borovecki, F., Lovrecic, L., Zhou, J., et al. (2005). Genome-wide expression profiling of human blood reveals biomarkers for Huntington’s disease. Proceedings of the National Academy of Sciences USA, 102(31), 1102311028. https://doi.org/10.1073/pnas.0504921102CrossRefGoogle ScholarPubMed
Bowden, J. A., Heckert, A., Ulmer, C. Z., et al. (2017). Harmonizing lipidomics: NIST interlaboratory comparison exercise for lipidomics using SRM 1950–Metabolites in frozen human plasma. Journal of Lipid Research, 58(12), 22752288. https://doi.org/10.1194/jlr.M079012Google Scholar
Bressler, J., Yu, B., Mosley, T. H., et al. (2017). Metabolomics and cognition in African American adults in midlife: The atherosclerosis risk in communities study. Translational Psychiatry, 7(7), e1173. https://doi.org/10.1038/tp.2017.118Google Scholar
Brier, M. R., Gordon, B., Friedrichsen, K et al. (2016). Tau and Aβ imaging, CSF measures, and cognition in Alzheimer’s disease. Science Translational Medicine, 8(338), 338ra366. https://doi.org/10.1126/scitranslmed.aaf2362Google Scholar
Casanova, R., Varma, S., Simpson, B., et al. (2016). Blood metabolite markers of preclinical Alzheimer’s disease in two longitudinally followed cohorts of older individuals. Alzheimer’s and Dementia, 12(7), 815822. https://doi.org/10.1016/j.jalz.2015.12.008CrossRefGoogle ScholarPubMed
Chahine, L. M., Stern, M. B., & Chen-Plotkin, A. (2014). Blood-based biomarkers for Parkinson’s disease. Parkinsonism and Related Disorders, 20, S99S103. https://doi.org/10.1016/S1353-8020(13)70025-7Google Scholar
Chan, P. H. (1996). Role of oxidants in ischemic brain damage. Stroke, 27(6), 11241129. https://doi.org/10.1161/01.STR.27.6.1124Google Scholar
Conesa, A., Madrigal, P., Tarazona, S., et al. (2016). A survey of best practices for RNA-seq data analysis. Genome Biology, 17(1), 13. https://doi.org/10.1186/s13059-016-0881-8Google Scholar
Dage, J. L., Wennberg, A. M. V., Airey, D. C., et al. (2016). Levels of tau protein in plasma are associated with neurodegeneration and cognitive function in a population-based elderly cohort. Alzheimer’s and Dementia, 12(12), 12261234. https://doi.org/10.1016/j.jalz.2016.06.001Google Scholar
Depp, C. A., & Jeste, D. V. (2006). Definitions and predictors of successful aging: A comprehensive review of larger quantitative studies. American Journal of Geriatric Psychiatry, 14(1), 620. https://doi.org/10.1097/01.JGP.0000192501.03069.bcGoogle Scholar
Edwards, M., Balldin, V. H., Hall, J., & O’Bryant, S. (2014). Combining select neuropsychological assessment with blood-based biomarkers to detect mild Alzheimer’s disease: A molecular neuropsychology approach. Journal of Alzheimer’s Disease, 42(2), 635640. https://doi.org/10.3233/JAD-140852CrossRefGoogle ScholarPubMed
Edwards, M., Balldin, V. H., Hall, J., & O’Bryant, S. (2015). Molecular markers of neuropsychological functioning and Alzheimer’s disease. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 1(1), 6166. https://doi.org/10.1016/j.dadm.2014.11.001Google Scholar
Ewers, M., Brendel, M., Rizk-Jackson, A., et al. (2014). Reduced FDG-PET brain metabolism and executive function predict clinical progression in elderly healthy subjects. NeuroImage: Clinical, 4, 4552. https://doi.org/10.1016/j.nicl.2013.10.018Google Scholar
Faden, A. I., & Loane, D. J. (2015). Chronic neurodegeneration after traumatic brain injury: Alzheimer disease, chronic traumatic encephalopathy, or persistent neuroinflammation? Neurotherapeutics, 12(1), 143150. https://doi.org/10.1007/s13311-014-0319-5CrossRefGoogle ScholarPubMed
Fehlbaum-Beurdeley, P., Sol, O., Désiré, L., et al. (2012). Validation of AclarusDx™, a blood-based transcriptomic signature for the diagnosis of Alzheimer’s disease. Journal of Alzheimer’s Disease, 32(1), 169181. https://doi.org/10.3233/JAD-2012-120637Google Scholar
Fiandaca, MS, Kapogiannis, D, Mapstone, M, Boxer, A, Eitan, E, Schwartz, JB, Abner, EL, Petersen, RC, Federoff, HJ, Miller, BL, Goetzl, EJ. Identification of preclinical Alzheimer’s disease by a profile of pathogenic proteins in neurally derived blood exosomes: A case-control study. Alzheimer’s & dementia : the journal of the Alzheimer’s Association. 2015;11(6):600–7. doi: 10.1016/j.jalz.2014.06.008. PubMed PMID: 25130657; PMCID: PMC4329112.CrossRefGoogle ScholarPubMed
Fiandaca, M. S. (1994). Surgical therapy for cerebral ischemia. In Fisher, M. (Ed.), Clinical Atlas of Cerebrovascular Disorders (pp. 117). London: Mosby.Google Scholar
Fiandaca, M. S., Mapstone, M. E., Cheema, A. K., & Federoff, H. J. (2014). The critical need for defining preclinical biomarkers in Alzheimer’s disease. Alzheimer’s and Dementia, 10(3), S196S212. https://doi.org/10.1016/j.jalz.2014.04.015Google Scholar
Fiandaca, M. S., Mapstone, M., Connors, E., et al. (2017). Systems healthcare: A holistic paradigm for tomorrow. BMC Systems Biology, 11(1), 142. https://doi.org/10.1186/s12918-017-0521-2Google Scholar
Fiandaca, M. S., & Wood, J. H. (1989). Diagnostic evaluation of cerebral and retinal ischemia. In Wood, J. H. (Ed.), Carotid Surgery, Vol. 1 (pp. 125). Philadelphia: Hanley & Belfus, Inc.Google Scholar
Fiandaca, M. S., Zhong, X., Cheema, A. K., et al. (2015). Plasma 24-metabolite panel predicts preclinical transition to clinical stages of Alzheimer’s disease. Frontiers in Neurology, 6, 237. https://doi.org/10.3389/fneur.2015.00237Google Scholar
Fiskum, G., Murphy, A. N., & Beal, M. F. (1999). Mitochondria in neurodegeneration: Acute ischemia and chronic neurodegenerative diseases. Journal of Cerebral Blood Flow and Metabolism, 19(4), 351369. https://doi.org/10.1097/00004647-199904000-00001Google Scholar
Fox, N. C., Warrington, E. K., Freeborough, P. A., et al. (1996). Presymptomatic hippocampal atrophy in Alzheimer’s disease: A longitudinal MRI study. Brain, 119(6), 20012007. https://doi.org/10.1093/brain/119.6.2001CrossRefGoogle ScholarPubMed
Franceschi, C., Bonafè, M., Valensin, S., et al. (2000). Inflamm‐aging: An evolutionary perspective on immunosenescence. Annals of the New York Academy of Sciences, 908(1), 244254. https://doi.org/10.1111/j.1749-6632.2000.tb06651.xGoogle Scholar
Frijhoff, J., Winyard, P. G., Zarkovic, N., et al. (2015). Clinical relevance of biomarkers of oxidative stress. Antioxidants and Redox Signaling, 23(14), 11441170. https://doi.org/10.1089/ars.2015.6317Google Scholar
Gefen, T., Shaw, E., Whitney, K., et al. (2014). Longitudinal neuropsychological performance of cognitive SuperAgers. Journal of the American Geriatrics Society, 62(8), 15981600. https://doi.org/10.1111/jgs.12967CrossRefGoogle ScholarPubMed
Gimeno, D., Marmot, M. G., & Singh-Manoux, A. (2008). Inflammatory markers and cognitive function in middle-aged adults: The Whitehall II study. Psychoneuroendocrinology, 33(10), 13221334. https://doi.org/10.1016/j.psyneuen.2008.07.006CrossRefGoogle ScholarPubMed
Gutchess, A. (2014). Plasticity of the aging brain: New directions in cognitive neuroscience. Science, 346(6209), 579582. https://doi.org/10.1126/science.1254604CrossRefGoogle ScholarPubMed
Harman, D. (1956). Aging: A theory based on free radical and radiation chemistry. Journal of Gerontology, 11, 298300. https://doi.org/10.1093/geronj/11.3.298Google Scholar
Harman, D. (1972). The biologic clock: The mitochondria? Journal of the American Geriatrics Society, 20(4), 145147. https://doi.org/10.1111/j.1532-5415.1972.tb00787.xGoogle Scholar
Harrison, T. M., Weintraub, S., Mesulam, M. M., & Rogalski, E. (2012). Superior memory and higher cortical volumes in unusually successful cognitive aging. Journal of the International Neuropsychological Society, 18(6), 10811085. https://doi.org/10.1017/S1355617712000847Google Scholar
Hayashi-Takagi, A., Vawter, M. P., & Iwamoto, K. (2014). Peripheral biomarkers revisited: Integrative profiling of peripheral samples for psychiatric research. Biological Psychiatry, 75(12), 920928. https://doi.org/10.1016/j.biopsych.2013.09.035Google Scholar
Henriksen, K., O’Bryant, S. E., Hampel, H., et al. (2014). The future of blood-based biomarkers for Alzheimer’s disease. Alzheimer’s and Dementia, 10(1), 115131. https://doi.org/10.1016/j.jalz.2013.01.013Google Scholar
Ho, L., Fivecoat, H., Wang, J., & Pasinetti, G. M. (2010). Alzheimer’s disease biomarker discovery in symptomatic and asymptomatic patients: Experimental approaches and future clinical applications. Experimental Gerontology, 45(1), 1522. https://doi.org/10.1016/j.exger.2009.09.007Google Scholar
Horvat, P., Kubinova, R., Pajak, A., et al. (2016). Blood-based oxidative stress markers and cognitive performance in early old age: The HAPIEE study. Dementia and Geriatric Cognitive Disorders, 42(5–6), 297309. https://doi.org/10.1159/000450702CrossRefGoogle ScholarPubMed
Hulstaert, F., Blennow, K., Ivanoiu, A., et al. (1999). Improved discrimination of AD patients using β-amyloid (1–42) and tau levels in CSF. Neurology, 52(8), 15551562. https://doi.org/10.1212/WNL.52.8.1555Google Scholar
Husseini, N. E., & Laskowitz, D. T. (2010). Clinical application of blood biomarkers in cerebrovascular disease. Expert Review of Neurotherapeutics, 10(2), 189203. https://doi.org/10.1586/ern.09.151CrossRefGoogle ScholarPubMed
Hyman, B. T., Phelps, C. H., Beach, T. G., et al. (2012). National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimer’s and Dementia, 8(1), 113. https://doi.org/10.1016/j.jalz.2011.10.007Google Scholar
Jack, C. R., Petersen, R. C., Xu, Y. C., et al. (1999). Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology, 52(7), 13971403. https://doi.org/10.1212/WNL.52.7.1397CrossRefGoogle ScholarPubMed
Jeromin, A., & Bowser, R. (2017). Biomarkers in neurodegenerative diseases. Neurodegenera-tive Diseases, 15, 491528. https://doi.org/10.1007/978-3-319-57193-5_20Google Scholar
Kiddle, S. J., Sattlecker, M., Proitsi, P., et al. (2014). Candidate blood proteome markers of Alzheimer’s disease onset and progression: A systematic review and replication study. Journal of Alzheimer’s Disease, 38(3), 515531. https://doi.org/10.3233/JAD-130380Google Scholar
Klaips, C. L., Jayaraj, G. G., & Hartl, F. U. (2018). Pathways of cellular proteostasis in aging and disease. Journal of Cell Biology, 217(1), 5163. https://doi.org/10.1083/jcb.201709072Google Scholar
Klunk, W. E., Engler, H., Nordberg, A., et al. (2004). Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound‐B. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 55(3), 306319. https://doi.org/10.1002/ana.20009Google Scholar
Leidinger, P., Backes, C., Deutscher, S., et al. (2013). A blood based 12-miRNA signature of Alzheimer disease patients. Genome Biology, 14(7), R78. https://doi.org/10.1186/gb-2013-14-7-r78Google Scholar
Lewczuk, P., Riederer, P., O’Bryant, S. E., et al. (2018). Cerebrospinal fluid and blood biomarkers for neurodegenerative dementias: An update of the consensus of the Task Force on Biological Markers in Psychiatry of the World Federation of Societies of Biological Psychiatry. World Journal of Biological Psychiatry, 19(4), 244328. https://doi.org/10.1080/15622975.2017.1375556Google Scholar
Lezak, M. D. (2012). Neuropsychological assessment, 5th ed. New York: Oxford University Press.Google Scholar
Li, D., Misialek, J. R., Boerwinkle, E., et al. (2017). Prospective associations of plasma phospholipids and mild cognitive impairment/dementia among African Americans in the ARIC Neurocognitive Study. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 6, 110. https://doi.org/10.1016/j.dadm.2016.09.003Google Scholar
Lim, A., Krajina, K., & Marsland, A. L. (2013). Peripheral inflammation and cognitive aging. In Halaris, A. & Leonard, B. E. (Eds.), Inflammation in psychiatry (pp. 175187). Basel: Karger Publishers.CrossRefGoogle Scholar
Lin, F., Ren, P., Mapstone, M., et al. (2017). The cingulate cortex of older adults with excellent memory capacity. Cortex, 86, 8392. https://doi.org/10.1016/j.cortex.2016.11.009Google Scholar
López-Otín, C., Galluzzi, L., Freije, J. M., Madeo, F., & Kroemer, G. (2016). Metabolic control of longevity. Cell, 166(4), 802821. https://doi.org/10.1016/j.cell.2016.07.031Google Scholar
Lu, T., Aron, L., Zullo, J., et al. (2014). REST and stress resistance in ageing and Alzheimer’s disease. Nature, 507(7493), 448454. /https://doi.org/10.1038/nature13163CrossRefGoogle ScholarPubMed
Mapstone, M., Cheema, A. K., Fiandaca, M. S., et al. (2014). Plasma phospholipids identify antecedent memory impairment in older adults. Nature Medicine, 20(4), 415418. https://doi.org/10.1038/nm.3466Google Scholar
Mapstone, M., Cheema, A. K., Zhong, X., Fiandaca, M. S., & Federoff, H. J. (2017a). Biomarker validation: Methods and matrix matter. Alzheimer’s and Dementia: Journal of the Alzheimer’s Association, 13(5), 608609. https://doi.org/10.1016/j.jalz.2016.11.004CrossRefGoogle ScholarPubMed
Mapstone, M., Lin, F., Nalls, M. A., et al. (2017b). What success can teach us about failure: The plasma metabolome of older adults with superior memory and lessons for Alzheimer’s disease. Neurobiology of Aging, 51, 148155. https://doi.org/10.1016/j.neurobiolaging.2016.11.007Google Scholar
Mayeux, R., & Schupf, N. (2011). Blood-based biomarkers for Alzheimer’s disease: Plasma Aβ40 and Aβ42, and genetic variants. Neurobiology of Aging, 32, S10S19. https://doi.org/10.1016/j.neurobiolaging.2011.09.004Google Scholar
McKhann, G. M., Knopman, D. S., Chertkow, H., et al. (2011). 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. Alzheimer’s and Dementia, 7(3), 263269. https://doi.org/10.1016/j.jalz.2011.03.005Google Scholar
Mergenthaler, P., Lindauer, U., Dienel, G. A., & Meisel, A. (2013). Sugar for the brain: The role of glucose in physiological and pathological brain function. Trends in Neurosciences, 36(10), 587597. https://doi.org/10.1016/j.tins.2013.07.001CrossRefGoogle ScholarPubMed
Mesulam, M. M. (2000). A plasticity‐based theory of the pathogenesis of Alzheimer’s disease. Annals of the New York Academy of Sciences, 924(1), 4252. https://doi.org/10.1111/j.1749-6632.2000.tb05559.xGoogle Scholar
Mullane, K., & Williams, M. (2013). Alzheimer’s therapeutics: Continued clinical failures question the validity of the amyloid hypothesis – but what lies beyond? Biochemical Pharmacology, 85(3), 289305. https://doi.org/10.1016/j.bcp.2012.11.014Google Scholar
Nyberg, L., Lövdén, M., Riklund, K., Lindenberger, U., & Bäckman, L. (2012). Memory aging and brain maintenance. Trends in Cognitive Sciences, 16(5), 292305. https://doi.org/10.1016/j.tics.2012.04.005Google Scholar
Oberdoerffer, P., & Sinclair, D. A. (2007). The role of nuclear architecture in genomic instability and ageing. Nature Reviews Molecular Cell Biology, 8(9), 692702. https://doi.org/10.1038/nrm2238CrossRefGoogle ScholarPubMed
O’Bryant, S. E., Edwards, M., Johnson, L., et al. (2016). A blood screening test for Alzheimer’s disease. Alzheimer’s and Dementia: Diagnosis, Assessment and Disease Monitoring, 3, 8390. https://doi.org/10.1016/j.dadm.2016.06.004Google Scholar
O’Bryant, S. E., Xiao, G., Barber, R., et al. (2010). A serum protein–based algorithm for the detection of Alzheimer disease. Archives of Neurology, 67(9), 10771081. https://doi.org/10.1001/archneurol.2010.215CrossRefGoogle ScholarPubMed
Pellerin, L., Bergersen, L. H., Halestrap, A. P., & Pierre, K. (2005). Cellular and subcellular distribution of monocarboxylate transporters in cultured brain cells and in the adult brain. Journal of Neuroscience Research, 79(1–2), 5564. https://doi.org/10.1002/jnr.20307CrossRefGoogle ScholarPubMed
Peterson, M. J., Geoghegan, S., & Lawhorne, L. W. (2018). An exploratory analysis of potential new biomarkers of cognitive function. Journals of Gerontology, Series A: Biological Sciences and Medical Sciences, 74(3), 299305. https://doi.org/10.1093/gerona/gly122Google Scholar
Petersen, R. C., Caracciolo, B., Brayne, C., et al. (2014). Mild cognitive impairment: A concept in evolution. Journal of Internal Medicine, 275(3), 214228. https://doi.org/10.1111/joim.12190CrossRefGoogle ScholarPubMed
Petersen, R. C., Stevens, J. C., Ganguli, M., et al. (2001). Practice parameter: Early detection of dementia: Mild cognitive impairment (an evidence-based review): Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology, 56(9), 11331142. https://doi.org/10.1212/WNL.56.9.1133Google Scholar
Picard, M., & McEwen, B. S. (2014). Mitochondria impact brain function and cognition. Proceedings of the National Academy of Sciences USA, 111(1), 78. https://doi.org/10.1073/pnas.1321881111Google Scholar
Porges, E. C., Woods, A. J., Edden, R. A., et al. (2017). Frontal gamma-aminobutyric acid concentrations are associated with cognitive performance in older adults. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 2(1), 3844. https://doi.org/10.1016/j.bpsc.2016.06.004Google Scholar
Rafnsson, S. B., Deary, I. J., Smith, F. B., et al. (2007). Cognitive decline and markers of inflammation and hemostasis: The Edinburgh Artery Study. Journal of the American Geriatrics Society, 55(5), 700707. https://doi.org/10.1111/j.1532-5415.2007.01158.xGoogle Scholar
Ray, S., Britschgi, M., Herbert, C., et al. (2007). Classification and prediction of clinical Alzheimer’s diagnosis based on plasma signaling proteins. Nature Medicine, 13(11), 13591362. https://doi.org/10.1038/nm1653Google Scholar
Rembach, A., Ryan, T. M., Roberts, B. R., et al. (2013). Progress towards a consensus on biomarkers for Alzheimer’s disease: A review of peripheral analytes. Biomarkers in Medicine, 7(4), 641662. https://doi.org/10.2217/bmm.13.59Google Scholar
Reynolds, C. A., Gatz, M., Prince, J. A., Berg, S., & Pedersen, N. L. (2010). Serum lipid levels and cognitive change in late life. Journal of the American Geriatrics Society, 58(3), 501509. https://doi.org/10.1111/j.1532-5415.2010.02739.xGoogle Scholar
Robelin, L., & Gonzalez De Aguilar, J. L. (2014). Blood biomarkers for amyotrophic lateral sclerosis: Myth or reality? BioMed Research International, 2014, 525097. https://doi.org/10.1155/2014/525097Google Scholar
Rogalski, E. J., Gefen, T., Shi, J., et al. (2013). Youthful memory capacity in old brains: Anatomic and genetic clues from the Northwestern SuperAging Project. Journal of Cognitive Neuroscience, 25(1), 2936. https://doi.org/10.1162/jocn_a_00300CrossRefGoogle ScholarPubMed
Rohart, F., Gautier, B., Singh, A., & Le Cao, K. A. (2017). mixOmics: An R package for ’omics feature selection and multiple data integration. PLoS Computational Biology, 13(11), e1005752. https://doi.org/10.1371/journal.pcbi.1005752Google Scholar
Salthouse, T. A. (2009). When does age-related cognitive decline begin? Neurobiology of Aging, 30(4), 507514. https://doi.org/10.1016/j.neurobiolaging.2008.09.023Google Scholar
Sato, Y., Suzuki, I., Nakamura, T., et al. (2012). Identification of a new plasma biomarker of Alzheimer’s disease using metabolomics technology. Journal of Lipid Research, 53(3), 567576. https://doi.org/10.1194/jlr.M022376Google Scholar
Schneider, P., Hampel, H., & Buerger, K. (2009). Biological marker candidates of Alzheimer’s disease in blood, plasma, and serum. CNS Neuroscience and Therapeutics, 15(4), 358374. https://doi.org/10.1111/j.1755-5949.2009.00104.xGoogle Scholar
Simpson, B. N., Kim, M., Chuang, Y. F., et al. (2016). Blood metabolite markers of cognitive performance and brain function in aging. Journal of Cerebral Blood Flow and Metabolism, 36(7), 12121223. https://doi.org/10.1177/0271678X15611678Google Scholar
Singh, A., Gautier, B., Shannon, C. P., et al. (2016). DIABLO – an integrative, multi-omics, multivariate method for multi-group classification. bioRxiv. https://doi.org/10.1101/067611Google Scholar
Small, S. A., Perera, G. M., DeLaPaz, R., Mayeux, R., & Stern, Y. (1999). Differential regional dysfunction of the hippocampal formation among elderly with memory decline and Alzheimer’s disease. Annals of Neurology: Official Journal of the American Neurological Association and the Child Neurology Society, 45(4), 466472. https://doi.org/10.1002/1531-8249(199904)45:4<466::AID-ANA8>3.0.CO;2-QGoogle Scholar
Son, J. H., Shim, J. H., Kim, K. H., Ha, J. Y., & Han, J. Y. (2012). Neuronal autophagy and neurodegenerative diseases. Experimental and Molecular Medicine, 44(2), 8998. https://doi.org/10.3858/emm.2012.44.2.031Google Scholar
Sperling, R. A., Aisen, P. S., Beckett, L. A., et al. (2011). 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 and Dementia, 7(3), 280292. https://doi.org/10.1016/j.jalz.2011.03.003Google Scholar
Stern, Y. (2012). Cognitive reserve in ageing and Alzheimer’s disease. Lancet Neurology, 11(11), 10061012. https://doi.org/10.1016/s1474-4422(12)70191-6Google Scholar
Sutherland, G. T., Janitz, M., & Kril, J. J. (2011). Understanding the pathogenesis of Alzheimer’s disease: Will RNA‐Seq realize the promise of transcriptomics? Journal of Neurochemistry, 116(6), 937946. https://doi.org/10.1111/j.1471-4159.2010.07157.xGoogle Scholar
Tan, L., Yu, J. T., Liu, Q. Y., et al. (2014). Circulating miR-125b as a biomarker of Alzheimer’s disease. Journal of the Neurological Sciences, 336(1–2), 5256. https://doi.org/10.1016/j.jns.2013.10.002Google Scholar
Thambisetty, M., Simmons, A., Velayudhan, L., et al. (2010). Association of plasma clusterin concentration with severity, pathology, and progression in Alzheimer disease. Archives of General Psychiatry, 67(7), 739748. https://doi.org/10.1001/archgenpsychiatry.2010.78Google Scholar
Tylee, D. S., Kawaguchi, D. M., & Glatt, S. J. (2013). On the outside, looking in: A review and evaluation of the comparability of blood and brain “‐omes.American Journal of Medical Genetics Part B: Neuropsychiatric Genetics, 162(7), 595603. https://doi.org/10.1002/ajmg.b.32150Google Scholar
Ubhi, B. K. (2018). Direct Infusion-Tandem Mass Spectrometry (DI-MS/MS) Analysis of Complex Lipids in Human Plasma and Serum Using the Lipidyzer™ Platform. In Giera, M. (Ed.), Clinical metabolomics (pp. 227236). New York: Humana Press.Google Scholar
Uttara, B., Singh, A. V., Zamboni, P., & Mahajan, R. T. (2009). Oxidative stress and neurodegenerative diseases: A review of upstream and downstream antioxidant therapeutic options. Current Neuropharmacology, 7(1), 6574. https://doi.org/10.2174/157015909787602823Google Scholar
Varma, V. R., Oommen, A. M., Varma, S., et al. (2018). Brain and blood metabolite signatures of pathology and progression in Alzheimer disease: A targeted metabolomics study. PLoS Medicine, 15(1), e1002482. https://doi.org/10.1371/journal.pmed.1002482Google Scholar
Veiga, S., Wahrheit, J., Rodríguez-Martín, A., & Sonntag, D. (2018). Quantitative metabolomics in Alzheimer’s disease: Technical considerations for improved reproducibility. In Sigurdsson, E. M., Calero, M., & Gasset, M. (Eds.), Amyloid proteins (pp. 463470). New York: Humana Press.Google Scholar
Wallace, D. C. (2010). Bioenergetics, the origins of complexity, and the ascent of man. Proceedings of the National Academy of Sciences USA, 107 (Suppl. 2), 89478953. https://doi.org/10.1073/pnas.0914635107Google Scholar
Wong, M. W., Braidy, N., Poljak, A., et al. (2017). Dysregulation of lipids in Alzheimer’s disease and their role as potential biomarkers. Alzheimer’s and Dementia, 13(7), 810827. https://doi.org/10.1016/j.jalz.2017.01.008Google Scholar
Zetterberg, H., Wilson, D., Andreasson, U., et al. (2013). Plasma tau levels in Alzheimer’s disease. Alzheimer’s Research and Therapy, 5(2), 9. https://doi.org/10.1186/alzrt163Google Scholar

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