The effect of diagnostic criteria on outcome measures in preclinical and prodromal Alzheimer's disease: Implications for trial design

Daniela Bertens, Betty M Tijms, Lisa Vermunt, Niels D Prins, Philip Scheltens, Pieter Jelle Visser, Daniela Bertens, Betty M Tijms, Lisa Vermunt, Niels D Prins, Philip Scheltens, Pieter Jelle Visser

Abstract

Introduction: We investigated the influence of different inclusion criteria for preclinical and prodromal Alzheimer's disease (AD) on changes in biomarkers and cognitive markers and on trial sample size estimates.

Methods: We selected 522 cognitively normal subjects and 872 subjects with mild cognitive impairment from the Alzheimer's Disease Neuroimaging Initiative study. Compared inclusion criteria were (1) preclinical or prodromal AD (amyloid marker abnormal); (2) preclinical or prodromal AD stage-1 (amyloid marker abnormal, injury marker normal); and (3) preclinical or prodromal AD stage-2 (amyloid and injury markers abnormal). Outcome measures were amyloid, neuronal injury, and cognitive markers.

Results: In both subjects with preclinical and prodromal AD stage-2, inclusion criteria resulted in the largest observed decline in brain volumetric measures on magnetic resonance imaging and cognitive markers.

Discussion: Inclusion criteria influence the observed rate of worsening in outcome measures. This has implications for trial design.

Keywords: Alzheimer's disease; Biomarkers; Clinical trial; Cognitive markers; Longitudinal; Preclinical; Prodromal; Sample size estimates.

Figures

Fig. 1
Fig. 1
Schematic overview of the groups according to subclassification, applying the research criteria. Abbreviations: ADNI, Alzheimer's Disease Neuroimaging Initiative; MCI, mild cognitive impairment; AD, Alzheimer's disease. Subject classification based on AD biomarkers: Preclinical AD, n = 146; 49 based on CSF measures only, 80 based on PET, and 17 subjects with both modalities present. Preclinical AD stage-1, n = 110; 33 based on CSF measures only, 60 based on PET, and 17 with both modalities present. Preclinical AD stage-2, n = 34; 16 based on CSF measures only, 17 based on PET, and 1 with both modalities present. For two cognitively normal subjects, we did not have any information of their injury status so they could not be further classified into stage-1 or stage-2. Prodromal AD, n = 420; 149 based on CSF measures only, 148 based on PET, and 123 with both modalities present. Prodromal AD stage-1, n = 216; 63 based on CSF measures only, 88 based on PET, and 65 with both modalities present. Prodromal AD stage-2, n = 197; 85 based on CSF measures, 59 based on PET, 53 with both modalities present. For seven MCI subjects, we did not have any information of their injury status, so they could not be further classified into stage-1 or stage-2.

References

    1. Sperling R.A., Aisen P.S., Beckett L.A., Bennett D.A., Craft S., Fagan A.M. 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 Dement. 2011;7:280–292.
    1. Dubois B., Feldman H.H., Jacova C., Hampel H., Molinuevo L., Blennow K. Position Paper Advancing research diagnostic criteria for Alzheimer's disease: the IWG-2 criteria. Lancet Neurol. 2014;13:614–629.
    1. Albert M.S., DeKosky S.T., Dickson D., Dubois B., Feldman H.H., Fox N.C. 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. 2011;7:270–279.
    1. Vemuri P., Wiste H.J., Weigand S.D., Knopman D.S., Trojanowski J.Q., Shaw L.M. Serial MRI and CSF biomarkers in normal aging, MCI, and AD. Neurology. 2010;75:143–151.
    1. Schott J.M., Bartlett J.W., Fox N.C., Barnes J. Increased brain atrophy rates in cognitively normal older adults with low cerebrospinal fluid A??1-42. Ann Neurol. 2010;68:825–834.
    1. Schott J.M., Bartlett J.W., Barnes J., Leung K.K., Ourselin S., Fox N.C. Reduced sample sizes for atrophy outcomes in Alzheimer's disease trials: Baseline adjustment. Neurobiol Aging. 2010;31:1452–1462. 1462.e1-2.
    1. McEvoy L.K., Edland S.D., Holland D., Hagler D.J., Roddey J.C., Fennema-Notestine C. Neuroimaging enrichment strategy for secondary prevention trials in Alzheimer disease. Alzheimer Dis Assoc Disord. 2010;24:269–277.
    1. Yu P., Dean R.A., Hall S.D., Qi Y., Sethuraman G., Willis B.A. Enriching amnestic mild cognitive impairment populations for clinical trials: optimal combination of biomarkers to predict conversion to dementia. J Alzheimers Dis. 2012;32:373–385.
    1. Holland D., McEvoy L.K., Desikan R.S., Dale A.M. Enrichment and stratification for predementia Alzheimer disease clinical trials. PLoS One. 2012;7:e47739.
    1. Holland D., McEvoy L.K., Dale A.M. Unbiased comparison of sample size estimates from longitudinal structural measures in ADNI. Hum Brain Mapp. 2012;33:2586–2602.
    1. Grill J.D., Di L., Lu P.H., Lee C., Ringman J., Apostolova L.G. Estimating sample sizes for predementia Alzheimer's trials based on the Alzheimer's Disease Neuroimaging Initiative. Neurobiol Aging. 2013;34:62–72.
    1. Barnes J., Bartlett J.W., Fox N.C., Schott J.M. Targeted recruitment using cerebrospinal fluid biomarkers: implications for Alzheimer's disease therapeutic trials. J Alzheimers Dis. 2013;34:431–437.
    1. Folstein M.F., Folstein S.E., McHugh P.R. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198.
    1. Berg L. Clinical Dementia Rating (CDR) Psychopharmacol Bull. 1988;24:637–639.
    1. Hachinski V.C., Iliff L.D., Zilhka E., Du Boulay G.H., McAllister V.L., Marshall J. Cerebral blood flow in dementia. Arch Neurol. 1975;32:632–637.
    1. Sheikh J.Y.J. Clinical Gerontology: A Guide to Assessment and Intervention. The Haworth Press; New York: 1986. Geriatric Depression Scale (GDS): recent evidence and development of a shorter version.
    1. Shaw L.M., Vanderstichele H., Knapik-Czajka M., Clark C.M., Aisen P.S., Petersen R.C. Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann Neurol. 2009;65:403–413.
    1. Clark C.M., Schneider J.A., Bedell B.J., Beach T.G., Bilker W.B., Mintun M.A. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA. 2011;305:275–283.
    1. Landau S.M., Harvey D., Madison C.M., Koeppe R.A., Reiman E.M., Foster N.L. Associations between cognitive, functional, and FDG-PET measures of decline in AD and MCI. Neurobiol Aging. 2011;32:1207–1218.
    1. Freeborough P.A., Fox N.C. The boundary shift integral: an accurate and robust measure of cerebral volume changes from registered repeat MRI. IEEE Trans Med Imaging. 1997;16:623–629.
    1. Leung K.K., Clarkson M.J., Bartlett J.W., Clegg S., Jack C.R., Jr., Weiner M.W. Robust atrophy rate measurement in Alzheimer's disease using multi-site serial MRI: tissue-specific intensity normalization and parameter selection. Neuroimage. 2010;50:516–523.
    1. Fischl B., Salat D.H., Busa E., Albert M., Dieterich M., Haselgrove C. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron. 2002;33:341–355.
    1. Rosen W.G., Mohs R.C., Davis K.L. A new rating scale for Alzheimer's disease. Am J Psychiatry. 1984;141:1356–1364.
    1. Hua X., Ching C.R.K., Mezher A., Gutman B.A., Hibar D.P., Bhatt P. MRI-based brain atrophy rates in ADNI phase 2: acceleration and enrichment considerations for clinical trials. Neurobiol Aging. 2016;37:26–37.
    1. Ard M.C., Edland S.D. Power Calculations for Clinical Trials in Alzheimer's Disease. J Alzheimers Dis. 2011;26:369–377.
    1. Van Rossum I.A., Visser P.J., Knol D.L., Van Der Flier W.M., Teunissen C.E., Barkhof F. Injury markers but not amyloid markers are associated with rapid progression from mild cognitive impairment to dementia in alzheimer's disease. J Alzheimers Dis. 2012;29:319–327.
    1. Desikan R.S., McEvoy L.K., Thompson W.K., Holland D., Brewer J.B., Aisen P.S. Amyloid-β–Associated Clinical Decline Occurs Only in the Presence of Elevated P-tau. Arch Neurol. 2012;69:709–713.
    1. Mormino E.C., Betensky R.A., Hedden T., Schultz A.P., Amariglio R.E., Rentz D.M. Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol. 2014;71:1379–1385.
    1. Toledo J.B., Weiner M.W., Wolk D.A., Da X., Chen K., Arnold S.E. Neuronal injury biomarkers and prognosis in ADNI subjects with normal cognition. Acta Neuropathol Commun. 2014;2:26.
    1. Vos S.J.B., Verhey F., Frölich L., Kornhuber J., Wiltfang J., Maier W. Prevalence and prognosis of Alzheimer's disease at the mild cognitive impairment stage. Brain. 2015;138:1327–1338.
    1. Fagan A.M., Xiong C., Jasielec M.S., Bateman R.J., Goate A.M., Benzinger T.L. Longitudinal change in CSF biomarkers in autosomal-dominant Alzheimer's disease. Sci Transl Med. 2014;6:226ra30.
    1. Bertens D., Knol D.L., Scheltens P., Visser P.J. Temporal evolution of biomarkers and cognitive markers in the asymptomatic, MCI, and dementia stage of Alzheimer's disease. Alzheimers Dement. 2015;11:511–522.
    1. Van Rossum I.A., Vos S., Handels R., Visser P.J. Biomarkers as predictors for conversion from mild cognitive impairment to Alzheimer-type dementia: Implications for trial design. J Alzheimers Dis. 2010;20:881–891.
    1. Mattsson N., Insel P.S., Donohue M., Landau S., Jagust W.J., Shaw L.M. Independent information from cerebrospinal fluid amyloid-β and florbetapir imaging in Alzheimer's disease. Brain. 2014;138:772–783.
    1. Insel P.S., Donohue M.C., Mackin R.S., Aisen P.S., Hansson O., Weiner M.W. Cognitive and functional changes associated with Abeta pathology and the progression to mild cognitive impairment. Neurobiol Aging. 2016;48:172–181.
    1. Fujishima M., Kawaguchi A., Maikusa N., Kuwano R., Iwatsubo T., Matsuda H. Sample Size Estimation for Alzheimer's Disease Trials from Japanese ADNI Serial Magnetic Resonance Imaging. J Alzheimers Dis. 2016;56:75–88.
    1. Mattsson N., Insel P.S., Donohue M., Jagust W., Sperling R., Aisen P. Predicting Reduction of Cerebrospinal Fluid β-Amyloid 42 in Cognitively Healthy Controls. JAMA Neurol. 2015;72:554–560.
    1. Jagust W.J., Landau S.M., Shaw L.M., Trojanowski J.Q., Koeppe R.A., Reiman E.M. Relationships between biomarkers in aging and dementia. Neurology. 2009;73:1193–1199.
    1. Landau S.M., Lu M., Joshi A.D., Pontecorvo M., Mintun M.A., Trojanowski J.Q. Comparing positron emission tomography imaging and cerebrospinal fluid measurements of β-amyloid. Ann Neurol. 2013;74:826–836.
    1. Palmqvist S., Zetterberg H., Blennow K., Vestberg S., Andreasson U., Brooks D.J. Accuracy of Brain Amyloid Detection in Clinical Practice Using Cerebrospinal Fluid β-Amyloid 42. JAMA Neurol. 2014;71:1282.
    1. Donohue M.C., Sperling R.A., Salmon D.P., Rentz D.M., Raman R., Thomas R.G. The preclinical Alzheimer cognitive composite: measuring amyloid-related decline. JAMA Neurol. 2014;71:961–970.

Source: PubMed

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