Advertisement

Robust Biomarkers: Methodologically Tracking Causal Processes in Alzheimer’s Measurement

  • Vadim KeyserEmail author
  • Louis Sarry
Chapter
  • 19 Downloads
Part of the Boston Studies in the Philosophy and History of Science book series (BSPS, volume 338)

Abstract

In biomedical measurement, biomarkers are used to achieve reliable prediction of, and useful causal information about, patient outcomes while minimizing complexity of measurement, resources, and invasiveness. In this paper we discuss a specific methodological use of clinical biomarkers in pharmacological measurement. We confront the reliability of clinical biomarkers that are used to gather information about clinically meaningful endpoints. Next, we present a systematic methodology for assessing the reliability of multiple surrogate markers (and biomarkers in general). We propose three relevant conditions for a robust methodology for biomarkers: (R1) Intervention-based demonstration of partial independence of modes; (R2) Comparison of diverging and converging results across biomarkers; and (R3) Information within the context of theory. Finally, we apply our robust methodology to currently developing Alzheimer’s research to make specific theoretical conclusions about promising causal culprits as well as decoupled biomarkers and endpoints.

References

  1. ALZFORUM. (n.d.). Networking for a cure. http://www.alzforum.org/therapeutics
  2. Aronson, J. K. (2005). Biomarkers and surrogate endpoints. British Journal of Clinical Pharmacology, 59(5), 491–494.  https://doi.org/10.1111/j.1365-2125.2005.02435.x.CrossRefGoogle Scholar
  3. Barad, K. (2007). Meeting the universe halfway. In Meeting the universe halfway (pp. 39–70). Duke University Press.  https://doi.org/10.1215/9780822388128-002
  4. Behl, C. (1997). Amyloid β-protein toxicity and oxidative stress in Alzheimers disease. Cell and Tissue Research, 290(3), 471–480.  https://doi.org/10.1007/s004410050955.CrossRefGoogle Scholar
  5. Brower, V. (2011). Biomarkers: Portents of malignancy. Nature, 471(7339), S19–S20.  https://doi.org/10.1038/471s19a.CrossRefGoogle Scholar
  6. Buyse, M., Molenberghs, G., Burzykowski, T., Renard, D., & Geys, H. (2000). The validation of surrogate endpoints in meta-analyses of randomized experiments. Biostatistics, 1(1), 49–67.  https://doi.org/10.1093/biostatistics/1.1.49.CrossRefGoogle Scholar
  7. Canevari, L., Abramov, A. Y., & Duchen, M. R. (2004). Toxicity of amyloid β peptide: Tales of calcium, mitochondria, and oxidative stress. Neurochemical Research, 29(3), 637–650.  https://doi.org/10.1023/b:nere.0000014834.06405.af.CrossRefGoogle Scholar
  8. Carrillo-Mora, P., Luna, R., & Colín-Barenque, L. (2014). Amyloid beta: Multiple mechanisms of toxicity and only some protective effects? Oxidative Medicine and Cellular Longevity, 2014, 1–15.  https://doi.org/10.1155/2014/795375.CrossRefGoogle Scholar
  9. Cleophas, T., Zwinderman, A., & Chaib, A. (2007). Novel procedures for validating surrogate endpoints in clinical trials. Current Clinical Pharmacology, 2(2), 123–128.  https://doi.org/10.2174/157488407780598126.CrossRefGoogle Scholar
  10. Cohn, J. N. (2004). Introduction to surrogate markers. Circulation, 109(25_suppl_1), IV–20–IV–21.  https://doi.org/10.1161/01.cir.0000133441.05780.1d.CrossRefGoogle Scholar
  11. Colombet, I., Pouchot, J., Kronz, V., Hanras, X., Capron, L., Durieux, P., & Wyplosz, B. (2010). Agreement between erythrocyte sedimentation rate and c-reactive protein in hospital practice. The American Journal of Medicine, 123(9), 863.e7–863.e13.  https://doi.org/10.1016/j.amjmed.2010.04.021.CrossRefGoogle Scholar
  12. Costenbader, K. H., Chibnik, L. B., & Schur, P. H. (2007). Discordance between erythrocyte sedimentation rate and c-reactive protein measurements: Clinical significance. Clinical and Experimental Rheumatology, 25(5), 746–749.Google Scholar
  13. Culp, S. (1994). Defending robustness: The bacterial mesosome as a test case. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association, 1994(1), 46–57.  https://doi.org/10.1086/psaprocbienmeetp.1994.1.193010.CrossRefGoogle Scholar
  14. Cummings, J. L., Dubois, B., Molinuevo, J. L., & Scheltens, P. (2013). International work group criteria for the diagnosis of Alzheimer disease. Medical Clinics of North America, 97(3), 363–368.  https://doi.org/10.1016/j.mcna.2013.01.001.CrossRefGoogle Scholar
  15. Cure, S., Abrams, K., Belger, M., Happich, M., & others. (2014). Systematic literature review and meta-analysis of diagnostic test accuracy in Alzheimer’s disease and other dementia using autopsy as standard of truth. Journal of Alzheimer’s Disease, 42(1), 169–182.CrossRefGoogle Scholar
  16. De Gruttola, V. G., Clax, P., DeMets, D. L., Downing, G. J., Ellenberg, S. S., Friedman, L., Gail, M. H., Prentice, R., Wittes, J., & Zeger, S. L. (2001). Considerations in the evaluation of surrogate endpoints in clinical trials. Controlled Clinical Trials, 22(5), 485–502.  https://doi.org/10.1016/s0197-2456(01)00153-2.CrossRefGoogle Scholar
  17. De Strooper, B., Vassar, R., & Golde, T. (2010). The secretases: Enzymes with therapeutic potential in Alzheimer disease. Nature Reviews Neurology, 6(2), 99–107.  https://doi.org/10.1038/nrneurol.2009.218.CrossRefGoogle Scholar
  18. Douglas, H. (2004). The irreducible complexity of objectivity. Synthese, 138(3), 453–473.  https://doi.org/10.1023/b:synt.0000016451.18182.91.CrossRefGoogle Scholar
  19. Downs, J. R., Clearfield, M., Weis, S., Whitney, E., Shapiro, D. R., Beere, P. A., Langendorfer, A., et al. (1998). Primary prevention of acute coronary events with Lovastatin in men and women with average cholesterol levels. JAMA, 279(20), 1615.  https://doi.org/10.1001/jama.279.20.1615.CrossRefGoogle Scholar
  20. Erickson, M. A., & Banks, W. A. (2013). Blood-brain barrier dysfunction as a cause and consequence of Alzheimer’s disease. Journal of Cerebral Blood Flow & Metabolism, 33(10), 1500–1513.  https://doi.org/10.1038/jcbfm.2013.135.CrossRefGoogle Scholar
  21. Feldman, M., Aziz, B., Kang, G. N., Opondo, M. A., Belz, R. K., & Sellers, C. (2013). C-reactive protein and erythrocyte sedimentation rate discordance: Frequency and causes in adults. Translational Research, 161(1), 37–43.  https://doi.org/10.1016/j.trsl.2012.07.006.CrossRefGoogle Scholar
  22. Fleming, T. R., & DeMets, D. L. (1996). Surrogate end points in clinical trials: Are we being misled? Annals of Internal Medicine, 125, 605–613.  https://doi.org/10.7326/0003- 4819-125-7-199610010-00011.
  23. Fleming, T. R., & Powers, J. H. (2012). Biomarkers and surrogate endpoints in clinical trials. Statistics in Medicine, 31(25), 2973–2984.  https://doi.org/10.1002/sim.5403.CrossRefGoogle Scholar
  24. Food and Drug Administration. (2017). FDA facts: Biomarkers and surrogate endpoints. http://www.fda.gov/AboutFDA/Innovation/ucm512503.htm
  25. Franklin, A. (1997). Calibration. Perspecties on Science, 5, 31–80.Google Scholar
  26. Garcia-Alloza, M., Subramanian, M., Thyssen, D., Borrelli, L. A., Fauq, A., Das, P., Golde, T. E., Hyman, B. T., & Bacskai, B. J. (2009). Existing plaques and neuritic abnormalities in APP:PS1 mice are not affected by administration of the gamma-secretase inhibitor LY-411575. Molecular Neurodegeneration, 4(1), 19.  https://doi.org/10.1186/1750-1326-4-19.CrossRefGoogle Scholar
  27. Giuffrida, M. L., Caraci, F., Pignataro, B., Cataldo, S., De Bona, P., Bruno, V., Molinaro, G., et al. (2009). β-Amyloid monomers are neuroprotective. Journal of Neuroscience, 29(34), 10582–10587.  https://doi.org/10.1523/jneurosci.1736-09.2009.CrossRefGoogle Scholar
  28. Glymour, C. (1980). Theory and evidence. Princeton: Princeton University Press.Google Scholar
  29. Gofman, J. W., Jones, H. B., Lindgren, F. T., Lyon, T. P., Elliott, H. A., & Strisower, B. (1950a). Blood lipids and human atherosclerosis. Circulation, 2(2), 161–178.  https://doi.org/10.1161/01.cir.2.2.161.CrossRefGoogle Scholar
  30. Gofman, J. W., Lindgren, F., Elliott, H., Mantz, W., Hewitt, J., Strisower, B., Herring, V., & Lyon, T. P. (1950b). The role of lipids and lipoproteins in atherosclerosis. Science, 111(2877), 166–186.  https://doi.org/10.1126/science.111.2877.166.CrossRefGoogle Scholar
  31. Hacking, I. (1983). Representing and intervening. Cambridge: Cambridge University Press.  https://doi.org/10.1017/cbo9780511814563.CrossRefGoogle Scholar
  32. Hampel, H., Bürger, K., Teipel, S. J., Bokde, A. L. W., Zetterberg, H., & Blennow, K. (2008). Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzheimers & Dementia, 4(1), 38–48.  https://doi.org/10.1016/j.jalz.2007.08.006.CrossRefGoogle Scholar
  33. Hardy, J., & Allsop, D. (1991). Amyloid deposition as the central event in the aetiology of alzheimers disease. Trends in Pharmacological Sciences, 12(January), 383–388.  https://doi.org/10.1016/0165-6147(91)90609-v. CrossRefGoogle Scholar
  34. Horwich, P. (2011). Probability and evidence. Cambridge University Press.  https://doi.org/10.1017/cbo9781316494219.
  35. Imbimbo, B. P., & Giardina, G. A. M. (2011). γ-secretase inhibitors and modulators for the treatment of Alzheimers disease: Disappointments and hopes. Current Topics in Medicinal Chemistry, 11(12), 1555–1570.  https://doi.org/10.2174/156802611795860942.CrossRefGoogle Scholar
  36. Institute of Medicine. (2010). Evaluation of biomarkers and surrogate endpoints in chronic disease. Washington, DC: National Academies Press.  https://doi.org/10.17226/12869. CrossRefGoogle Scholar
  37. Jack, C. R., & Holtzman, D. M. (2013). Biomarker modeling of Alzheimer’s disease. Neuron, 80(6), 1347–1358.  https://doi.org/10.1016/j.neuron.2013.12.003.CrossRefGoogle Scholar
  38. Justus, J. (2012). The elusive basis of inferential robustness. Philosophy of Science, 79(5), 795–807.  https://doi.org/10.1086/667902.CrossRefGoogle Scholar
  39. Katz, R. (2004). Biomarkers and surrogate markers: An FDA perspective. NeuroRX, 1(2), 189–195.  https://doi.org/10.1602/neurorx.1.2.189.CrossRefGoogle Scholar
  40. Keeley, B. L. (2002). Making sense of the senses. Journal of Philosophy, 99(1), 5–28.  https://doi.org/10.5840/jphil20029915.. Edited by John Smylie.CrossRefGoogle Scholar
  41. Keyser, V. (2016). A new theory of robust measurement. http://www.apaonline.org/members/group_content_view.asp?group=110424&id=476093
  42. Krut, J. J., Zetterberg, H., Blennow, K., Cinque, P., Hagberg, L., Price, R. W., Studahl, M., & Gisslén, M. (2012). Cerebrospinal fluid Alzheimers biomarker profiles in CNS infections. Journal of Neurology, 260(2), 620–626.  https://doi.org/10.1007/s00415-012-6688-y.CrossRefGoogle Scholar
  43. Landau, S. M., Lu, M., Joshi, A. D., Pontecorvo, M., Mintun, M. A., Trojanowski, J. Q., Shaw, L. M., Jagust, W. J., et al. (2013). Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid. Annals of Neurology, 74(6), 826–836.  https://doi.org/10.1002/ana.23908.CrossRefGoogle Scholar
  44. LaRosa, J. C., Grundy, S. M., Waters, D. D., Shear, C., Barter, P., Fruchart, J.-C., Gotto, A. M., et al. (2005). Intensive lipid lowering with atorvastatin in patients with stable coronary disease. New England Journal of Medicine, 352(14), 1425–1435.  https://doi.org/10.1056/nejmoa050461.CrossRefGoogle Scholar
  45. Lassere, M. N. (2007). The biomarker-surrogacy evaluation schema: A review of the biomarker-surrogate literature and a proposal for a criterion-based, quantitative, multidimensional hierarchical levels of evidence schema for evaluating the status of biomarkers as surrogate endpoints. Statistical Methods in Medical Research, 17(3), 303–340.  https://doi.org/10.1177/0962280207082719.CrossRefGoogle Scholar
  46. Lassere, M. N., Johnson, K. R., Boers, M., Tugwell, P., Brooks, P., Simon, L., Strand, V., et al. (2007). Definitions and validation criteria for biomarkers and surrogate endpoints: Development and testing of a quantitative hierarchical levels of evidence schema. The Journal of Rheumatology, 34(3), 607–615.Google Scholar
  47. Lee, H.-g., Zhu, X., Nunomura, A., Perry, G., & Smith, M. A. (2006). Amyloid-β vaccination: Testing the amyloid hypothesis? The American Journal of Pathology, 169(3), 738–739.  https://doi.org/10.2353/ajpath.2006.060633.CrossRefGoogle Scholar
  48. Lehmann, S., Dumurgier, J., Schraen, S., Wallon, D., Blanc, F., Magnin, E., Bombois, S., et al. (2014). A diagnostic scale for Alzheimer’s disease based on cerebrospinal fluid biomarker profiles. Alzheimers Research & Therapy, 6(3), 38.  https://doi.org/10.1186/alzrt267.CrossRefGoogle Scholar
  49. Lesne, S. (2014). Toxic oligomer species of amyloid-β in Alzheimers disease, a timing issue. Swiss Medical Weekly, November.  https://doi.org/10.4414/smw.2014.14021.
  50. Levins, R. (1966). The strategy of model building in population biology. American Scientist, 54(4), 421–431.Google Scholar
  51. Lloyd, E. A. (2010). Confirmation and robustness of climate models. Philosophy of Science, 77(5), 971–984.  https://doi.org/10.1086/657427.CrossRefGoogle Scholar
  52. Marnell, L., Mold, C., & Du Clos, T. W. (2005). C-reactive protein: Ligands, receptors and role in inflammation. Clinical Immunology, 117(2), 104–111.  https://doi.org/10.1016/ j.clim.2005.08.004.CrossRefGoogle Scholar
  53. Mattsson, N., Insel, P. S., Donohue, M., Landau, S., Jagust, W. J., Shaw, L. M., Trojanowski, J. Q., Zetterberg, H., Blennow, K., & Weiner, M. W. (2014). Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimers disease. Brain, 138(3), 772–783.  https://doi.org/10.1093/brain/awu367.CrossRefGoogle Scholar
  54. Mayeux, R. (2004). Biomarkers: Potential uses and limitations. NeuroRX, 1(2), 182–188.  https://doi.org/10.1602/neurorx.1.2.182.CrossRefGoogle Scholar
  55. McConkey, B., Davies, P., Crockson, R. A., Crockson, A. P., Butler, M., Constable, T. J., & Amos, R. S. (1979). Effects of gold, dapsone, and prednisone on serum c-reactive protein and haptoglobin and the erythrocyte sedimentation rate in rheumatoid arthritis. Annals of the Rheumatic Diseases, 38(2), 141–144.  https://doi.org/10.1136/ard.38.2.141.CrossRefGoogle Scholar
  56. Mo, J.-A., Lim, J.-H., Sul, A.-R., Lee, M., Youn, Y. C., & Kim, H.-J. (2015). Cerebrospinal fluid β-Amyloid142 levels in the differential diagnosis of Alzheimer’s disease systematic review and meta-analysis. PLOS One, 10(2), e0116802.  https://doi.org/10.1371/journal.pone.0116802. Edited by Rosanna Squitti.CrossRefGoogle Scholar
  57. Musiek, E. S., & Holtzman, D. M. (2012). Origins of Alzheimer’s disease. Current Opinion in Neurology, 25(6), 715–720.  https://doi.org/10.1097/wco.0b013e32835a30f4.CrossRefGoogle Scholar
  58. Orzack, S. H., & Sober, E. (1993). A critical assessment of Levinss the strategy of model building in population biology (1966). The Quarterly Review of Biology, 68(4), 533–546.  https://doi.org/10.1086/418301.CrossRefGoogle Scholar
  59. Otvos, J. D., Mora, S., Shalaurova, I., Greenland, P., Mackey, R. H., & Goff, D. C. (2011). Clinical implications of discordance between low-density lipoprotein cholesterol and particle number. Journal of Clinical Lipidology, 5(2), 105–113.  https://doi.org/10.1016/j.jacl.2011.02.001.CrossRefGoogle Scholar
  60. Palmqvist, S., Zetterberg, H., Mattsson, N., Johansson, P., Minthon, L., Blennow, K., Olsson, M., & Hansson, O. (2015). Detailed comparison of amyloid PET and CSF biomarkers for identifying early Alzheimer disease. Neurology, 85(14), 1240–1249.  https://doi.org/10.1212/wnl.0000000000001991.CrossRefGoogle Scholar
  61. Pepe, M. S., Janes, H., Li, C. I., Bossuyt, P. M., Feng, Z., & Hilden, J. (2016). Early-phase studies of biomarkers: What target sensitivity and specificity values might confer clinical utility? Clinical Chemistry, 62(5), 737–742.  https://doi.org/10.1373/clinchem.2015.252163.CrossRefGoogle Scholar
  62. Prentice, R. L. (1989). Surrogate endpoints in clinical trials: Definition and operational criteria. Statistics in Medicine, 8(4), 431–440.  https://doi.org/10.1002/sim.4780080407.CrossRefGoogle Scholar
  63. Qu, Y. (2013). Statistical evaluation of surrogate markers: Validity, efficiency, and sensitivity. Clinical Trials: Journal of the Society for Clinical Trials, 10(5), 693–695.  https://doi.org/10.1177/1740774513499652.CrossRefGoogle Scholar
  64. Ratner, M. (2015). Biogens early Alzheimers data raise hopes, some eyebrows. Nature Biotechnology, 33(5), 438–438.  https://doi.org/10.1038/nbt0515-438.CrossRefGoogle Scholar
  65. Reiman, E. M. (2016). Attack on amyloid-β protein. Nature, 537(7618), 36–37.  https://doi.org/10.1038/537036a.CrossRefGoogle Scholar
  66. Ridker, P. M., Danielson, E., Fonseca, F. A. H., Genest, J., Gotto, A. M., Kastelein, J. J. P., Koenig, W., et al. (2008). Rosuvastatin to prevent vascular events in men and women with elevated c-reactive protein. New England Journal of Medicine, 359(21), 2195–2207.  https://doi.org/10.1056/nejmoa0807646.CrossRefGoogle Scholar
  67. Ritchie, C., Smailagic, N., Noel-Storr, A. H., Takwoingi, Y., Flicker, L., Mason, S. E., & McShane, R. (2014). Plasma and cerebrospinal fluid amyloid beta for the diagnosis of Alzheimer’s disease dementia and other dementias in people with mild cognitive impairment (MCI). The Cochrane Database of Systematic Reviews, 6. CD008782,  https://doi.org/10.1002/14651858.CD008782.pub4.
  68. Ritchie, K., Carrière, I., Berr, C., Amieva, H., Dartigues, J.-F., Ancelin, M.-L., & Ritchie, C. W. (2016). The clinical picture of Alzheimer’s disease in the decade before diagnosis (Vol. 77, pp. e305–e311). The Journal of Clinical Psychiatry.  https://doi.org/10.4088/jcp.15m09989.
  69. Sachdeva, A., Cannon, C. P., Deedwania, P. C., LaBresh, K. A., Smith, S. C., Dai, D., Hernandez, A., & Fonarow, G. C. (2009). Lipid levels in patients hospitalized with coronary Artery disease: An analysis of 136,905 hospitalizations in get with the guidelines. American Heart Journal, 157(1), 111–117.e2.  https://doi.org/10.1016/j.ahj.2008.08.010.CrossRefGoogle Scholar
  70. Savva, G. M., Wharton, S. B., Ince, P. G., Forster, G., Matthews, F. E., & Brayne, C. (2009). Age, neuropathology, and dementia. New England Journal of Medicine, 360(22), 2302–2309.  https://doi.org/10.1056/nejmoa0806142.CrossRefGoogle Scholar
  71. Sbong, S., & Feldman, M. (2014). Frequency and causes of c-reactive protein and erythrocyte sedimentation rate disagreements in adults. International Journal of Rheumatic Diseases, 18(1), 29–32.  https://doi.org/10.1111/1756-185x.12537.CrossRefGoogle Scholar
  72. Schneider, L. S., Kennedy, R. E., & Cutter, G. R. (2010). Requiring an amyloid-β1-42 biomarker for prodromal Alzheimers disease or Mild cognitive impairment does not lead to more efficient clinical trials. Alzheimers & Dementia, 6(5), 367–377.  https://doi.org/10.1016/j.jalz.2010.07.004.CrossRefGoogle Scholar
  73. Schupbach, J. N. (2016). Robustness analysis as explanatory reasoning. The British Journal for the Philosophy of Science, axw008.  https://doi.org/10.1093/bjps/axw008
  74. Selkoe, D. J., & Hardy, J. (2016). The amyloid hypothesis of Alzheimers disease at 25 years. EMBO Molecular Medicine, 8(6), 595–608.  https://doi.org/10.15252/emmm.201606210. CrossRefGoogle Scholar
  75. Sevigny, J., Chiao, P., Bussière, T., Weinreb, P. H., Williams, L., Maier, M., Dunstan, R., et al. (2016). The antibody aducanumab reduces aβ plaques in Alzheimer’s disease. Nature, 537(7618), 50–56.  https://doi.org/10.1038/nature19323.CrossRefGoogle Scholar
  76. Sober, E. (1989). Independent evidence about a common cause. Philosophy of Science, 56(2), 275–287.  https://doi.org/10.1086/289487.CrossRefGoogle Scholar
  77. Sperling, R. A., Jack, C. R., Black, S. E., Frosch, M. P., Greenberg, S. M., Hyman, B. T., Scheltens, P., et al. (2011). Amyloid-related imaging abnormalities in amyloid-modifying therapeutic trials: Recommendations from the Alzheimer’s association research roundtable workgroup. Alzheimers & Dementia, 7(4), 367–385.  https://doi.org/10.1016/j.jalz.2011.05.2351.CrossRefGoogle Scholar
  78. Staley, K. W. (2004). Robust evidence and secure evidence claims. Philosophy of Science, 71(4), 467–488.  https://doi.org/10.1086/423748.CrossRefGoogle Scholar
  79. Stegenga, J. (2009). Robustness, discordance, and relevance. Philosophy of Science, 76(5), 650–661.  https://doi.org/10.1086/605819.CrossRefGoogle Scholar
  80. Stegenga, J. (2012). Rerum concordia discors: Robustness and discordant multimodal evidence. In Characterizing the robustness of science (pp. 207–226). Dordrecht: Springer.  https://doi.org/10.1007/978-94-007-2759-5_9.CrossRefGoogle Scholar
  81. Terry, R. D., Masliah, E., Salmon, D. P., Butters, N., DeTeresa, R., Hill, R., Hansen, L. A., & Katzman, R. (1991). Physical basis of cognitive Alterations in Alzheimers disease: Synapse loss is the major correlate of cognitive impairment. Annals of Neurology, 30(4), 572–580.  https://doi.org/10.1002/ana.410300410.CrossRefGoogle Scholar
  82. Toledo, J. B., Weiner, M. W., Wolk, D. A., Da, X., Chen, K., Arnold, S. E., Jagust, W., et al. (2014). Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition. Acta Neuropathologica Communications, 2(1).  https://doi.org/10.1186/2051-5960-2-26.
  83. Toyn, J. (2015). What lessons can be learned from failed Alzheimer’s disease trials? Expert Review of Clinical Pharmacology, 8(3), 267–269.  https://doi.org/10.1586/17512433.2015.1034690.CrossRefGoogle Scholar
  84. Trout, J. D. (1998). Measuring the intentional world. Oxford University Press.  https://doi.org/10.1093/0195107667.001.0001.
  85. Van Fraassen, B. C. (2008). Scientific representation: Paradoxes of perspective. Oxford: Oxford University Press.Google Scholar
  86. Vos, S. J. B., Gordon, B. A., Yi, S., Visser, P. J., Holtzman, D. M., Morris, J. C., Fagan, A. M., & Benzinger, T. L. S. (2016). NIA-AA staging of preclinical Alzheimer disease: Discordance and concordance of CSF and imaging biomarkers. Neurobiology of Aging, 44(August), 1–8.  https://doi.org/10.1016/j.neurobiolaging.2016.03.025.CrossRefGoogle Scholar
  87. Walsh, D. M., & Selkoe, D. J. (2007). Aβ oligomers – a decade of discovery. Journal of Neurochemistry, 101(5), 1172–1184.  https://doi.org/10.1111/j.1471-4159.2006.04426.x.CrossRefGoogle Scholar
  88. Walsh, D. M., Tseng, B. P., Rydel, R. E., Podlisny, M. B., & Selkoe, D. J. (2000). The oligomerization of amyloid β-protein begins intracellularly in cells derived from human brain. Biochemistry, 39(35), 10831–10839.  https://doi.org/10.1021/bi001048s.CrossRefGoogle Scholar
  89. Weisberg, M. (2006). Robustness analysis. Philosophy of Science, 73(5), 730–742.  https://doi.org/10.1086/518628.CrossRefGoogle Scholar
  90. Willem, M., Garratt, A. N., Novak, B., Citron, M., Kaufmann, S., Rittger, A., DeStrooper, B., Saftig, P., Birchmeier, C., & Haass, C. (2006). Control of peripheral nerve myelination by the -secretase BACE1. Science, 314(5799), 664–666.  https://doi.org/10.1126/science.1132341.CrossRefGoogle Scholar
  91. Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Cambridge, MA: Harvard University Press.Google Scholar
  92. Wolz, R., Schwarz, A. J., Gray, K. R., Yu, P., & Hill, D. L. G. (2016). Enrichment of clinical trials in MCI due to AD using markers of amyloid and neurodegeneration. Neurology, 87(12), 1235–1241.  https://doi.org/10.1212/wnl.0000000000003126.CrossRefGoogle Scholar
  93. Woo, H.-N., Park, J.-S., Gwon, A.-R., Arumugam, T. V., & Jo, D.-G. (2009). Alzheimer’s disease and notch signaling. Biochemical and Biophysical Research Communications, 390(4), 1093–1097.  https://doi.org/10.1016/j.bbrc.2009.10.093.CrossRefGoogle Scholar
  94. Woodward, J. (2004). Making things happen: A counterfactual theory of causal explanation. Oxford: Oxford University Press.  https://doi.org/10.1093/0195155270.003.0005. CrossRefGoogle Scholar
  95. Woodward, J. (2006). Some varieties of robustness. Journal of Economic Methodology, 13(2), 219–240.  https://doi.org/10.1080/13501780600733376.CrossRefGoogle Scholar
  96. Young, A. L., Oxtoby, N. P., Daga, P., Cash, D. M., Fox, N. C., Ourselin, S., Schott, J. M., & Alexander, D. C. (2014). A data-driven model of biomarker changes in sporadic Alzheimers disease. Brain, 137(9), 2564–2577.  https://doi.org/10.1093/brain/awu176.CrossRefGoogle Scholar
  97. Younkin, Steven G. 1995. Evidence that aβ42 is the real culprit in Alzheimers disease. Annals of Neurology 37 (3): 287–288.  https://doi.org/10.1002/ana.410370303.
  98. Zwan, M., van Harten, A., Ossenkoppele, R., Bouwman, F., Teunissen, C., Adriaanse, S., Lammertsma, A., Scheltens, P., van Berckel, B. N. M., & Van der Flier, W. (2013). Concordance between CSF biomarkers and [11c]PIB PET in a memory clinic population. Alzheimers & Dementia, 9(4), P830.  https://doi.org/10.1016/j.jalz.2013.04.476.CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.California State UniversityFresnoUSA

Personalised recommendations